{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/huseinzol05/forecast-bitcoin-based-on-polarity-using-lstm/notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "寻找投资的有效前沿：核心思想是找到每只股票的最佳权重，以形成具有最高夏普比率的理想投资组合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. 问题背景"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "问题：预测股票价格（成交量）     \n",
    "分析：历史数据对现在时刻存在影响     \n",
    "解决方法：采用时间序列模型（RNN、LSTM）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，金融数据分析最重要的一点就是从场景定义模型，而不是从模型定义场景，   \n",
    "你想要预测股价，那就采用回归方法，预测成交量或公司持续盈利能力也是这样。   \n",
    "但是如果你想根据市场情况判断明天该买入或卖出，这显示不是一个简单的回归或分类问题，你需要结合RL方法进行学习（这期实战已出过视频）。     \n",
    "然后，如上个视频中讲到的，主要解决股票交易、投资组合管理等问题。   \n",
    "最后，在金融数据分析中label是很少见的，也没有大量数据来支撑这个label是可学习的。    \n",
    "真实场景中不是所有问题都能找到label或者明确用分类还是回归的，不像深度学习的demo    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(252, 7)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>778.200012</td>\n",
       "      <td>781.650024</td>\n",
       "      <td>763.450012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>1872400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-03</td>\n",
       "      <td>767.250000</td>\n",
       "      <td>769.950012</td>\n",
       "      <td>759.030029</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>1943200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>750.659973</td>\n",
       "      <td>770.359985</td>\n",
       "      <td>750.560974</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>2134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>774.500000</td>\n",
       "      <td>785.190002</td>\n",
       "      <td>772.549988</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>1585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>783.400024</td>\n",
       "      <td>795.632996</td>\n",
       "      <td>780.190002</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>1350800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>779.940002</td>\n",
       "      <td>791.226990</td>\n",
       "      <td>771.669983</td>\n",
       "      <td>785.309998</td>\n",
       "      <td>785.309998</td>\n",
       "      <td>2607100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>791.169983</td>\n",
       "      <td>791.169983</td>\n",
       "      <td>752.179993</td>\n",
       "      <td>762.559998</td>\n",
       "      <td>762.559998</td>\n",
       "      <td>4745200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>756.539978</td>\n",
       "      <td>760.780029</td>\n",
       "      <td>750.380005</td>\n",
       "      <td>754.020020</td>\n",
       "      <td>754.020020</td>\n",
       "      <td>2431800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2016-11-14</td>\n",
       "      <td>755.599976</td>\n",
       "      <td>757.849976</td>\n",
       "      <td>727.539978</td>\n",
       "      <td>736.080017</td>\n",
       "      <td>736.080017</td>\n",
       "      <td>3631700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2016-11-15</td>\n",
       "      <td>746.969971</td>\n",
       "      <td>764.416016</td>\n",
       "      <td>746.969971</td>\n",
       "      <td>758.489990</td>\n",
       "      <td>758.489990</td>\n",
       "      <td>2384000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2016-11-02  778.200012  781.650024  763.450012  768.700012  768.700012   \n",
       "1  2016-11-03  767.250000  769.950012  759.030029  762.130005  762.130005   \n",
       "2  2016-11-04  750.659973  770.359985  750.560974  762.020020  762.020020   \n",
       "3  2016-11-07  774.500000  785.190002  772.549988  782.520020  782.520020   \n",
       "4  2016-11-08  783.400024  795.632996  780.190002  790.510010  790.510010   \n",
       "5  2016-11-09  779.940002  791.226990  771.669983  785.309998  785.309998   \n",
       "6  2016-11-10  791.169983  791.169983  752.179993  762.559998  762.559998   \n",
       "7  2016-11-11  756.539978  760.780029  750.380005  754.020020  754.020020   \n",
       "8  2016-11-14  755.599976  757.849976  727.539978  736.080017  736.080017   \n",
       "9  2016-11-15  746.969971  764.416016  746.969971  758.489990  758.489990   \n",
       "\n",
       "    Volume  \n",
       "0  1872400  \n",
       "1  1943200  \n",
       "2  2134800  \n",
       "3  1585100  \n",
       "4  1350800  \n",
       "5  2607100  \n",
       "6  4745200  \n",
       "7  2431800  \n",
       "8  3631700  \n",
       "9  2384000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/rlData.csv')\n",
    "print(df.shape)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 数据预处理（特征工程）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))    \n",
    "X_scaled = X_std * (max - min) + min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.112708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.090008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.089628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.160459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.188066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.112708\n",
       "1  0.090008\n",
       "2  0.089628\n",
       "3  0.160459\n",
       "4  0.188066"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先将收盘价转换为整数\n",
    "data = df.iloc[:, 4:5].astype('float32')\n",
    "# 将数据放缩至给定区间\n",
    "# 思考：为什么要进行数据缩放预处理？\n",
    "df_log = MinMaxScaler().fit(data).transform(data)   \n",
    "df_log = pd.DataFrame(df_log)\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 数据集的划分\n",
    "    训练集：从开始时间到最近 30 天的历史股价（收盘价）变动数据\n",
    "    验证集：过去 30 天的股价（收盘价）变动数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((222, 1), (30, 1))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size = 30\n",
    "simulation_size = 5\n",
    "\n",
    "df_train = df_log.iloc[ : -test_size]\n",
    "df_test = df_log.iloc[-test_size : ]\n",
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 模型的构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.tensorflow.org/api_docs/python/tf/compat/v1/nn/rnn_cell/LSTMCell   \n",
    "https://www.tensorflow.org/api_docs/python/tf/compat/v1/nn/dynamic_rnn   \n",
    "关于forget_bias: http://proceedings.mlr.press/v37/jozefowicz15.pdf    \n",
    "    非零值：一定程度上提高准确率；避免过拟合"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        learning_rate,\n",
    "        num_layers,\n",
    "        size,\n",
    "        size_layer,\n",
    "        output_size,\n",
    "        forget_bias = 0.1,\n",
    "    ):\n",
    "        def lstm_cell(size_layer):\n",
    "            return tf.nn.rnn_cell.LSTMCell(size_layer, state_is_tuple = False)\n",
    "\n",
    "        rnn_cells = tf.nn.rnn_cell.MultiRNNCell(\n",
    "            [lstm_cell(size_layer) for _ in range(num_layers)],\n",
    "            state_is_tuple = False,\n",
    "        )\n",
    "        # self.X为什么要用三维？\n",
    "        self.X = tf.placeholder(tf.float32, (None, None, size))\n",
    "        self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
    "        drop = tf.contrib.rnn.DropoutWrapper(\n",
    "            rnn_cells, output_keep_prob = forget_bias\n",
    "        )\n",
    "        self.hidden_layer = tf.placeholder(\n",
    "            tf.float32, (None, num_layers * 2 * size_layer)\n",
    "        )\n",
    "#         tf.compat.v1.nn.dynamic_rnn(\n",
    "#         cell, inputs, sequence_length=None, initial_state=None, dtype=None,\n",
    "#         parallel_iterations=None, swap_memory=False, time_major=False, scope=None\n",
    "#         )\n",
    "        self.outputs, self.last_state = tf.nn.dynamic_rnn(\n",
    "            drop, self.X, initial_state = self.hidden_layer, dtype = tf.float32\n",
    "        )\n",
    "        self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
    "        self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(\n",
    "            self.cost\n",
    "        )\n",
    "     \n",
    " # 标准偏差\n",
    "def calculate_accuracy(real, predict):\n",
    "    real = np.array(real) + 1\n",
    "    predict = np.array(predict) + 1\n",
    "    percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real)))\n",
    "    return percentage * 100\n",
    "\n",
    "def anchor(signal, weight):\n",
    "    buffer = []\n",
    "    last = signal[0]\n",
    "    for i in signal:\n",
    "        smoothed_val = last * weight + (1 - weight) * i\n",
    "        buffer.append(smoothed_val)\n",
    "        last = smoothed_val\n",
    "    return buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_layers = 1\n",
    "size_layer = 128\n",
    "timestamp = 5\n",
    "epoch = 200\n",
    "dropout_rate = 0.8\n",
    "future_day = test_size\n",
    "learning_rate = 0.02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型损失函数、优化器的选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 模型的训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.112708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.090008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.089628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.160459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.188066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.112708\n",
       "1  0.090008\n",
       "2  0.089628\n",
       "3  0.160459\n",
       "4  0.188066"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax = MinMaxScaler().fit(df.iloc[:, 4:5].astype('float32')) # Close index\n",
    "df_log = minmax.transform(df.iloc[:, 4:5].astype('float32')) # Close index\n",
    "df_log = pd.DataFrame(df_log)\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forecast():\n",
    "    tf.reset_default_graph()\n",
    "    modelnn = Model(\n",
    "        learning_rate, num_layers, df_log.shape[1], size_layer, df_log.shape[1], dropout_rate\n",
    "    )\n",
    "    sess = tf.InteractiveSession()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    date_ori = pd.to_datetime(df.iloc[:, 0]).tolist()\n",
    "\n",
    "    pbar = tqdm(range(epoch), desc = 'train loop')\n",
    "    for i in pbar:\n",
    "        # C0, h0初始化为0\n",
    "        init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
    "        total_loss, total_acc = [], []\n",
    "        # timestamp = 5，相当于设置batchsize\n",
    "        for k in range(0, df_train.shape[0] - 1, timestamp):\n",
    "            index = min(k + timestamp, df_train.shape[0] - 1)\n",
    "            batch_x = np.expand_dims(\n",
    "                df_train.iloc[k : index, :].values, axis = 0\n",
    "            )\n",
    "            batch_y = df_train.iloc[k + 1 : index + 1, :].values\n",
    "#             self.X = tf.placeholder(tf.float32, (None, None, size))\n",
    "#             self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
    "            logits, last_state, _, loss = sess.run(\n",
    "                [modelnn.logits, modelnn.last_state, modelnn.optimizer, modelnn.cost],\n",
    "                feed_dict = {\n",
    "                    modelnn.X: batch_x,\n",
    "                    modelnn.Y: batch_y,\n",
    "                    modelnn.hidden_layer: init_value,\n",
    "                },\n",
    "            )        \n",
    "            init_value = last_state\n",
    "            total_loss.append(loss)\n",
    "            total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0]))\n",
    "        pbar.set_postfix(cost = np.mean(total_loss), acc = np.mean(total_acc))\n",
    "    \n",
    "    future_day = test_size\n",
    "\n",
    "    output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1]))\n",
    "    output_predict[0] = df_train.iloc[0]\n",
    "    upper_b = (df_train.shape[0] // timestamp) * timestamp\n",
    "    # 数组广播机制：广播会在缺失或长度为1的轴上进行\n",
    "    init_value = np.zeros((1, num_layers * 2 * size_layer))\n",
    "\n",
    "    for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp):\n",
    "        out_logits, last_state = sess.run(\n",
    "            [modelnn.logits, modelnn.last_state],\n",
    "            feed_dict = {\n",
    "                modelnn.X: np.expand_dims(\n",
    "                    df_train.iloc[k : k + timestamp], axis = 0\n",
    "                ),\n",
    "                modelnn.hidden_layer: init_value,\n",
    "            },\n",
    "        )\n",
    "        init_value = last_state\n",
    "        output_predict[k + 1 : k + timestamp + 1] = out_logits\n",
    "\n",
    "    if upper_b != df_train.shape[0]:\n",
    "        out_logits, last_state = sess.run(\n",
    "            [modelnn.logits, modelnn.last_state],\n",
    "            feed_dict = {\n",
    "                modelnn.X: np.expand_dims(df_train.iloc[upper_b:], axis = 0),\n",
    "                modelnn.hidden_layer: init_value,\n",
    "            },\n",
    "        )\n",
    "        output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits\n",
    "        future_day -= 1\n",
    "        date_ori.append(date_ori[-1] + timedelta(days = 1))\n",
    "\n",
    "    init_value = last_state\n",
    "    \n",
    "    for i in range(future_day):\n",
    "        o = output_predict[-future_day - timestamp + i:-future_day + i]\n",
    "        out_logits, last_state = sess.run(\n",
    "            [modelnn.logits, modelnn.last_state],\n",
    "            feed_dict = {\n",
    "                modelnn.X: np.expand_dims(o, axis = 0),\n",
    "                modelnn.hidden_layer: init_value,\n",
    "            },\n",
    "        )\n",
    "        init_value = last_state\n",
    "        output_predict[-future_day + i] = out_logits[-1]\n",
    "        date_ori.append(date_ori[-1] + timedelta(days = 1))\n",
    "    \n",
    "    output_predict = minmax.inverse_transform(output_predict)\n",
    "    deep_future = anchor(output_predict[:, 0], 0.3)\n",
    "    \n",
    "    return deep_future[-test_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 模型的调参"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 模型的验证（性能评估）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 1\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x0000018DB0D58390>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\application\\anaconda3\\envs\\qchen\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1711: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n",
      "train loop: 100%|████████████████████████████████████████████| 200/200 [01:08<00:00,  2.96it/s, acc=96.7, cost=0.00304]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 2\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x0000018DB09D8828>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\application\\anaconda3\\envs\\qchen\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1711: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n",
      "train loop: 100%|████████████████████████████████████████████| 200/200 [01:07<00:00,  3.04it/s, acc=96.3, cost=0.00381]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 3\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x0000018DB1455AC8>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\application\\anaconda3\\envs\\qchen\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1711: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n",
      "train loop: 100%|████████████████████████████████████████████| 200/200 [01:07<00:00,  2.91it/s, acc=96.3, cost=0.00445]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 4\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x0000018DB2B5EBA8>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\application\\anaconda3\\envs\\qchen\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1711: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n",
      "train loop: 100%|████████████████████████████████████████████| 200/200 [01:07<00:00,  2.97it/s, acc=96.4, cost=0.00395]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 5\n",
      "WARNING:tensorflow:<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x0000018DB3080710>: Using a concatenated state is slower and will soon be deprecated.  Use state_is_tuple=True.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\application\\anaconda3\\envs\\qchen\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1711: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
      "  warnings.warn('An interactive session is already active. This can '\n",
      "train loop: 100%|████████████████████████████████████████████| 200/200 [01:07<00:00,  2.95it/s, acc=96.7, cost=0.00307]\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for i in range(simulation_size):\n",
    "    print('simulation %d'%(i + 1))\n",
    "    results.append(forecast())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AVq1aRdeuXfnHP/6Bv7+/q8O6bHSfORERERERuapYa5k7dy7BwcF89dVXzJw5k48//viqSuSgFMmcMWa2MeZnY0xmsTJfY8wKY0yW8/k6Z/nDxpgM5+MLY0xosffcZYz5zhizzRgzqmy6IyIiIiIi17JffvmFnj170q9fP4KDg8nIyGDw4MEl7iDv7kozMvcecNdpZaOAVdbaIGCV82eA74HbrLUhwARgJoAxxgP4O3A30Bz4kzFGO3CIiIiIiMhl8/HHH9OyZUsWL17Mq6++SnJyMo0aNXJ1WGXmvMmctfZT4MBpxX8ETm7bOAfo7qz7hbX2V2f5V0Bd5+t2wDZr7Q5r7XFgvrMNERERERGRS3Lw4EEGDx7MfffdR506dVi3bh3PP/88Hh4erg6tTF3smrk61tofAZzP15dQ51FgmfN1AJBd7NgeZ5mIiIiIiMhF+/TTTwkJCWH27Nm88MILrF27lpCQEFeHdUWUyQYoxphOOJK5kSeLSqhW4j0RjDFDjDFpxpi0/fv3l0V4l2zKlCk0a9aMhx9+2NWhAJCens7SpUtLPLZixQratGlDcHAwbdq0YfXq1Vc4OhERERG5EgoLC3n66ad5+umnmTlzJikpKfz666/nf6ObOnr0KCNGjOD222/Hw8ODTz/9lIkTJ1KpUiVXh3bFXOytCfYZY/yttT8aY/yBn08eMMaEAP8A7rbW5jiL9wD1ir2/LvBDSQ1ba2fiXGsXHh5eLm+CN336dJYtW0ZgYGCp6hcUFFCxYtndBSI9PZ20tDS6du16xjE/Pz8WL17MjTfeSGZmJtHR0ezdu7fMYhERERER10hLS2PKlCl4enqSn59fVO7v70+LFi3OeNSoUcOF0V6a9PR0+vbtS2ZmJkOHDuVvf/sbPj4+rg7rirvYDOMjoD8wyfn8PwBjTH3gP0Bfa+3WYvXXAUHGmEBgL/AQ0Odig3aloUOHsmPHDrp168YjjzxC//79eeSRR9ixYwfe3t7MnDmTkJAQYmJi+OGHH9i5cyd+fn7MnTuXUaNGkZyczLFjx3jyySd57LHHAJg8eTJz586lQoUK3H333UyaNIlZs2Yxc+ZMjh8/zk033cTcuXPx9vZmwYIFjB8/Hg8PD2rUqMHKlSsZO3YsR44cISUlhRdeeIHevXsXxduqVaui1y1atODo0aMcO3bsmvrGQkRERORakJSUhDGGPXv2cPjwYTZu3MimTZvYuHEjGzduZNasWRw+fLiofkBAwBkJXvPmzalevboLe3FuBQUFTJ48mZiYGPz8/Fi6dCl33323q8NymfMmc8aYfwO3A37GmD3AOBxJ3IfGmEeB3cDJW6iPBWoB051bfxZYa8OttQXGmGFAIuABzLbWbrzcnbkSZsyYwfLly1mzZg1+fn4MHz6cVq1akZCQwOrVq+nXrx/p6ekArF+/npSUFKpUqcLMmTOpUaMG69at49ixY0RGRhIVFcWWLVtISEggNTUVb29vDhxw7DXTo0cPBg8eDMDo0aOJi4tj+PDhxMbGkpiYSEBAALm5uXh5eREbG0taWhrTpk07Z+yLFi2iVatWSuRERERErkKJiYm0bt2a6693bGfRsGFD7rnnnqLjJ06cYNeuXUXJ3cnHjBkzOHLkSFG9evXqlZjkuXrkKysri/79+/Pll1/Sq1cvpk+fTq1atVwak6udN5mz1v7pLIe6lFB3EDDoLO0sBUpe2HWxlo2Cn769rE1yQzDcPanU1VNSUli0aBEAnTt3Jicnh7y8PAC6detGlSpVAMc3JRkZGSxcuBCAvLw8srKyWLlyJQMHDsTb2xsAX19fADIzMxk9ejS5ubkcOnSI6OhoACIjIxkwYAC9evWiR48epY5z48aNjBw5kqSkpFK/R0RERETcQ15eHl999RUjR448a50KFSoQGBhIYGAg9957b1F5YWEhO3fuPCPJW7NmDceOHSuq16BBg6LErkWLFgQEBFClShW8vb2pUqXKGa8v1zIjay0zZszgueeew8vLi3//+9889NBDl6Vtd1d2C7muEdaeuazv5A0Jq1atekq9qVOnFiVlJy1fvrzEGxgOGDCAhIQEQkNDee+990hOTgYcI4OpqaksWbKEsLCwolHAc9mzZw/3338/77//Po0bN76Q7omIiIiIG1i9ejWFhYVERUVd8Hs9PDxo3LgxjRs3plu3bkXlhYWF7Nix44wkb+XKlRw/fvy87Xp6ep4z2SvpdUllc+fOJTExkaioKGbPnk1AgDbFP8m9k7kLGEErKx07dmTevHmMGTOG5ORk/Pz8SpxnHB0dzTvvvEPnzp3x9PRk69atBAQEEBUVRWxsLH369CmaZunr68vBgwfx9/cnPz+fefPmFf3Sbt++nYiICCIiIli8eDHZ2dlUq1aNgwcPlhhfbm4u99xzD6+88gqRkZFlei1ERERExDWSkpLw8fGhQ4cOl61NDw8PgoKCCAoKonv37kXlBQUFbN++nf3793P48GGOHDnCkSNHLuh1Xl4eP/744xnHjx49ekYc3t7eTJ8+naFDh5Y4CHItc+9krhyIiYlh4MCBhISE4O3tzZw5c0qsN2jQIHbu3Enr1q2x1lK7dm0SEhK46667SE9PJzw8HC8vL7p27crEiROZMGECERERNGjQgODg4KJkbcSIEWRlZWGtpUuXLoSGhlK/fn0mTZpEWFjYGRugTJs2jW3btjFhwgQmTJgAOP5jPzmXWkRERETcm7WWxMREOnXqhJeXV5mfr2LFitx8883cfPPNl73tEydOcPTo0VOSvFq1al3za+POxpQ0TbC8CA8Pt2lpaaeUbd68mWbNmrkooqubrq2IiIiI+9m2bRtBQUFMmzaNJ5980tXhyGVmjFlvrQ0v6ViZ3DRcRERERESujMTERICLWi8n7k3JnIiIiIiIG0tMTCQwMJCbbrrJ1aHIFaZkTkRERETETR0/fpw1a9YQFRWlzUGuQUrmRERERETc1JdffnnKPYnl2qJkTkRERETETSUlJeHh4UHnzp1dHYq4gJI5ERERERE3lZiYSPv27alRo4arQxEXUDJ3EaZMmUKzZs14+OGHXR0KAOnp6SxdurTEY2vXriUsLIywsDBCQ0P573//e4WjExEREZGysH//fr7++mtNsbyG6abhF2H69OksW7aMwMDAUtUvKCigYsWyu9Tp6emkpaXRtWvXM461bNmStLQ0KlasyI8//khoaCj33XdfmcYjIiIiImVv5cqVWGt1S4JrmEbmLtDQoUPZsWMH3bp148033+TAgQN0796dkJAQ2rdvT0ZGBgAxMTEMGTKEqKgo+vXrR2FhISNGjKBt27aEhITw7rvvFrU5efJkgoODCQ0NZdSoUQDMmjWLtm3bEhoaygMPPMDhw4cBWLBgAS1btiQ0NJSOHTty/Phxxo4dS3x8PGFhYcTHx58Sr7e3d1HidvToUe1yJCIiInKVSEpK4rrrriM8vMT7Scs1QMMzF2jGjBksX76cNWvW4Ofnx/Dhw2nVqhUJCQmsXr2afv36kZ6eDsD69etJSUmhSpUqzJw5kxo1arBu3TqOHTtGZGQkUVFRbNmyhYSEBFJTU/H29ubAgQMA9OjRg8GDBwMwevRo4uLiGD58OLGxsSQmJhIQEEBubi5eXl7ExsaSlpbGtGnTSow5NTWVRx55hF27djF37lyNyomIiIi4OWstSUlJ3HHHHXh4eLg6HHERt/5U/+raV9lyYMtlbbOpb1NGthtZ6vopKSksWrQIgM6dO5OTk0NeXh4A3bp1o0qVKoDjm5OMjAwWLlwIQF5eHllZWaxcuZKBAwfi7e0NgK+vLwCZmZmMHj2a3NzcU7abjYyMZMCAAfTq1YsePXqUKsaIiAg2btzI5s2b6d+/P3fffTeVK1cudR9FREREpHzZuHEjP/zwg9bLXeM0zfISWWvPKDs5lbFq1aqn1Js6dSrp6emkp6fz/fffExUVhbW2xKmPAwYMYNq0aXz77beMGzeOo0ePAo6RwZdffpns7GzCwsLIyckpdazNmjWjatWqZGZmXmg3RURERKQcSUxMBNB6uWucW4/MXcgIWlnp2LEj8+bNY8yYMSQnJ+Pn50f16tXPqBcdHc0777xD586d8fT0ZOvWrQQEBBAVFUVsbCx9+vQpmmbp6+vLwYMH8ff3Jz8/n3nz5hEQEADA9u3biYiIICIigsWLF5OdnU21atU4ePBgifF9//331KtXj4oVK7Jr1y6+++47GjZsWJaXRERERETKWFJSEs2aNaNevXquDkVcyK2TufIgJiaGgQMHEhISgre3N3PmzCmx3qBBg9i5cyetW7fGWkvt2rVJSEjgrrvuIj09nfDwcLy8vOjatSsTJ05kwoQJRERE0KBBA4KDg4uStREjRpCVlYW1li5duhAaGkr9+vWZNGkSYWFhvPDCC/Tu3bvovCkpKUyaNAlPT08qVKjA9OnT8fPzuyLXRkREREQuvyNHjvDpp5/y2GOPuToUcTFT0jTB8iI8PNympaWdUrZ582aaNWvmooiubrq2IiIiIuVfUlIS0dHRLF26lLvvvtvV4UgZM8ast9aWuGWp1syJiIiIiLiRxMREvLy86Nixo6tDERdTMiciIiIi4kaSkpK49dZbT9lsT65NSuZERERERNzE3r17yczM1C6WAiiZExERERFxGytWrADQ/eUEUDInIiIiIuI2EhMTqVOnDsHBwa4ORcoBJXMiIiIiIm7gxIkTrFixgqioKCpU0Md4UTJ3UaZMmUKzZs14+OGHXR0KAOnp6SxduvScdXbv3o2Pjw+vvfbaFYpKRERERC6nr7/+mpycHK2XkyK6afhFmD59OsuWLSMwMLBU9QsKCqhYsewudXp6OmlpaXTt2vWsdZ555hndh0RERETEjSUlJQFw5513ujgSKS80MneBhg4dyo4dO+jWrRtvvvkmBw4coHv37oSEhNC+fXsyMjIAiImJYciQIURFRdGvXz8KCwsZMWIEbdu2JSQkhHfffbeozcmTJxMcHExoaCijRo0CYNasWbRt25bQ0FAeeOABDh8+DMCCBQto2bIloaGhdOzYkePHjzN27Fji4+MJCwsjPj7+jJgTEhJo1KgRLVq0uAJXSERERETKQmJiImFhYdSpU8fVoUg5oZG5CzRjxgyWL1/OmjVr8PPzY/jw4bRq1YqEhARWr15Nv379SE9PB2D9+vWkpKRQpUoVZs6cSY0aNVi3bh3Hjh0jMjKSqKgotmzZQkJCAqmpqXh7e3PgwAEAevToweDBgwEYPXo0cXFxDB8+nNjYWBITEwkICCA3NxcvLy9iY2NJS0tj2rRpZ8T7+++/8+qrr7JixQpNsRQRERFxUwcPHuSLL77g2WefdXUoUo64dTL308SJHNu85bK2WalZU2548cVS109JSWHRokUAdO7cmZycHPLy8gDo1q0bVapUARzD4hkZGSxcuBCAvLw8srKyWLlyJQMHDsTb2xsAX19fADIzMxk9ejS5ubkcOnSoaPvZyMhIBgwYQK9evejRo8d54xs3bhzPPPMMPj4+pe6TiIiIiJQva9asoaCgQOvl5BRuncyVB9baM8qMMQBUrVr1lHpTp049454gy5cvL6pf3IABA0hISCA0NJT33nuP5ORkwDEymJqaypIlSwgLCysaBTyb1NRUFi5cyPPPP09ubi4VKlSgcuXKDBs27EK7KiIiIiIukpSUhLe3N5GRka4ORcoRt07mLmQErax07NiRefPmMWbMGJKTk/Hz86N69epn1IuOjuadd96hc+fOeHp6snXrVgICAoiKiiI2NpY+ffoUTbP09fXl4MGD+Pv7k5+fz7x58wgICABg+/btREREEBERweLFi8nOzqZatWocPHiwxPg+++yzotcxMTH4+PgokRMRERFxM4mJidx+++1UqlTJ1aFIOXLeDVCMMbONMT8bYzKLlfkaY1YYY7Kcz9c5y40xZooxZpsxJsMY07rYe/o762cZY/qXTXeuvJiYGNLS0ggJCWHUqFHMmTOnxHqDBg2iefPmtG7dmpYtW/LYY49RUFDAXXfdRbdu3QgPDycsLKxoXduECROIiIjgzjvvpGnTpkXtjBgxguDgYFq2bEnHjh0JDQ2lU6dObNq06awboIiIiOyd8UkAACAASURBVIiI+9qxYwfbtm07Y4aXiClpmuApFYzpCBwC3rfWtnSWTQYOWGsnGWNGAddZa0caY7oCw4GuQATwtrU2whjjC6QB4YAF1gNtrLW/nuvc4eHhNi0t7ZSyzZs306xZs4voqpyPrq2IiIhI+TNjxgwef/xxNm/efMqX/HJtMMast9aGl3TsvCNz1tpPgQOnFf8RODkENQfoXqz8fevwFVDTGOMPRAMrrLUHnAncCuCuC++KiIiIiMi1JSkpifr163PzzTe7OhQpZy72PnN1rLU/Ajifr3eWBwDZxertcZadrfwMxpghxpg0Y0za/v37LzI8ERERERH3l5+fz6pVq4iKiipx0zy5tl3um4aX9Btmz1F+ZqG1M6214dba8Nq1a1/W4ERERERE3MnatWv57bfftF5OSnSxydw+5/RJnM8/O8v3APWK1asL/HCOchEREREROYvExEQqVKhAly5dXB2KlEMXm8x9BJzckbI/8L9i5f2cu1q2B/Kc0zATgShjzHXOnS+jnGUiIiIiInIWSUlJtGvXjuuuu87VoUg5VJpbE/wb+BK42RizxxjzKDAJuNMYkwXc6fwZYCmwA9gGzAKeALDWHgAmAOucj1hnmYiIiIiIlODAgQOsW7dOUyzlrM5703Br7Z/OcuiMsV7ruM/Bk2dpZzYw+4KiK6emTJnCO++8Q+vWrZk3b56rwyE9PZ0ffviBrl27nnFs586dNGvWrGj3o/bt2zNjxowrHaKIiIiIXKBVq1Zx4sQJoqKiXB2KlFPnTebkTNOnT2fZsmUEBgaWqn5BQQEVK5bdpU5PTyctLa3EZA6gcePGpKenl9n5RUREROTyS0xMpEaNGrRr187VoUg5dbl3s7zqDR06lB07dtCtWzfefPNNDhw4QPfu3QkJCaF9+/ZkZGQAEBMTw5AhQ4iKiqJfv34UFhYyYsQI2rZtS0hICO+++25Rm5MnTyY4OJjQ0FBGjRoFwKxZs2jbti2hoaE88MADHD58GIAFCxbQsmVLQkND6dixI8ePH2fs2LHEx8cTFhZGfHz8lb8oIiIiInJZWWtJSkqiS5cuZTooIO5NvxkXaMaMGSxfvpw1a9bg5+fH8OHDadWqFQkJCaxevZp+/foVjYKtX7+elJQUqlSpwsyZM6lRowbr1q3j2LFjREZGEhUVxZYtW0hISCA1NRVvb28OHHAsJezRoweDBw8GYPTo0cTFxTF8+HBiY2NJTEwkICCA3NxcvLy8iI2NJS0tjWnTppUY8/fff0+rVq2oXr06L7/8MrfeeuuVuVgiIiIiclG2bNlCdnY2o0ePdnUoUo65dTL32Ydb+SX70GVt06+eD7f2alLq+ikpKSxatAiAzp07k5OTQ15eHgDdunWjSpUqgGMnooyMDBYuXAhAXl4eWVlZrFy5koEDB+Lt7Q2Ar68vAJmZmYwePZrc3FwOHTpUtPA1MjKSAQMG0KtXL3r06HHe+Pz9/dm9eze1atVi/fr1dO/enY0bN1K9evVS91FERERErqykpCQArZeTc3LrZK48cOz5cipjHPdIr1q16in1pk6desZuRMuXLy+qX9yAAQNISEggNDSU9957j+TkZMAxMpiamsqSJUsICws771q4SpUqUalSJQDatGlD48aN2bp1K+Hh4RfUTxERERG5chITE2nSpAkNGzZ0dShSjrl1MnchI2hlpWPHjsybN48xY8aQnJyMn59fiaNe0dHRvPPOO3Tu3BlPT0+2bt1KQEAAUVFRxMbG0qdPn6Jplr6+vhw8eBB/f3/y8/OZN28eAQEBAGzfvp2IiAgiIiJYvHgx2dnZVKtWjYMHD5YY3/79+/H19cXDw4MdO3aQlZVFo0aNyvSaiIiIiMjFO3bsGMnJyTz66KOuDkXKObdO5sqDmJgYBg4cSEhICN7e3syZM6fEeoMGDWLnzp20bt0aay21a9cmISGBu+66i/T0dMLDw/Hy8qJr165MnDiRCRMmEBERQYMGDQgODi5K1kaMGEFWVhbWWrp06UJoaCj169dn0qRJhIWF8cILL9C7d++i83766aeMHTuWihUr4uHhwYwZM4qmcoqIiIhI+ZOSksKRI0d0fzk5L1PSNMHyIjw83KalpZ1StnnzZpo1a+aiiK5uurYiIiIirjdy5MiiXdN9fHxcHY64mDFmvbW2xDVSGpkTERERt3T8+HEOHjzIoUOHzvlcUllISAijR4/WB2UplxITE4mMjNTvp5yXkjkRERE5p9zcXI4ePUpBQQGFhYUUFBSc8vr059KWlXTs8OHDpU7Q8vPzSxW/MYZq1arh4+NDtWrVqFKlCsuWLSM+Pp6ZM2dy5513lvEVFCm9n376iW+++YaJEye6OhRxA0rmREREBHBsurBlyxYyMjKKHt988w379u27onF4eXlRrVq1UxKwatWqceONNxb9fL7n05O303eOTklJYdCgQURFRTFw4EBef/11rrvuuivaT5GSrFixAkDr5aRUlMyJiIhcY6y1/Pjjj6ckbBkZGWzZsoWCggLAcWubli1b0rVrV5o3b46Pjw8VK1Ys2lCrNM8XU6dKlSp4eXmV+TW45ZZbSE9PJzY2lsmTJ7Ns2TKmT5/O/fffX+bnFjmXpKQkateuTVhYmKtDETegZE5EROQqduTIETZt2lSUsJ185OTkFNWpX78+ISEhdOvWjZCQEEJCQggKCqJixav7Y0LlypWZOHEiDz74II8++ig9evTgwQcfZOrUqdxwww2uDk+uQSdOnCApKYk777yTChUquDoccQNX9/+lRURErhHWWnbv3n1KwpaRkcHWrVs5ceIEAN7e3gQHB9OjRw9CQkIIDQ0lODiYmjVrujh612rdujVr167ltddeY/z48axatYq33nqLvn37njE9U6QsZWRk8PPPPxMVFeXqUMRNKJm7QLm5uXzwwQc88cQTZdJ+cnIyXl5e/OEPfyiT9t977z3S0tKYNm1ambQvIiJl6+QUyaysLL777rtTEre8vLyieo0aNSIkJIRevXoVJW6NGjXSt/1n4enpyQsvvMD999/Po48+Sv/+/fnggw949913adCggavDk2tEYmIigJI5KTUlcxcoNzeX6dOnl5jMFRYW4uHhcUntJycn4+PjU2IyV1BQcNVPeRERKStJSUns2LGDunXrUq9ePerWrYuvr2+5HHmx1rJv3z6ysrKKHtu2bSt6/v3334vqVqtWjZCQEPr06UNoaCghISG0bNmSatWqubAH7qtp06Z89tlnTJ8+nVGjRtGyZUsmTZrE448/rkRYylxiYiLBwcH4+/u7OhRxE8oMLtCoUaPYvn07YWFh3Hnnndxzzz2MHz8ef39/0tPTWbp0Kffeey+ZmZkAvPbaaxw6dIiYmBi2b9/Ok08+yf79+/H29mbWrFk0bdq0qO2dO3cyY8YMPDw8+Ne//sXUqVOJi4vD19eXDRs20Lp1a2JjYxk+fDjffvstBQUFxMTE8Mc//pH33nuPjz76iMOHD7N9+3buv/9+Jk+eDMA///lPXnnlFfz9/WnSpAmVKlVyybUTEXGVjz/+mPvuu++M8sqVK5+S3J18FP/Zz8+vTBI+ay379+8/JWErnrQdOnSoqG7FihVp1KgRN910E7fffjtBQUEEBQXRpEkTGjRoUC4TUndWoUIFhg0bxr333stjjz3GsGHDmD9/Pv/4xz+4+eabXR2eXKV+//13UlJSePrpp10dirgRJXMXaNKkSWRmZpKeng44RtLWrl1LZmYmgYGB7Ny586zvHTJkCDNmzCAoKIjU1FSeeOIJVq9eXXS8YcOGDB06FB8fH5577jkA4uLi2Lp1KytXrsTDw4MXX3yRzp07M3v2bHJzc2nXrh133HEHAOnp6WzYsIFKlSpx8803M3z4cCpWrMi4ceNYv349NWrUoFOnTrRq1arsLpCISDmzY8cO+vbtS6tWrVi0aBE///wze/bsITs7mz179hQ9PvnkE3744Yei3RxPqlSp0lkTvZM/+/n5lThqY63ll19+OSVJK560/fbbb0V1PTw8CAwMJCgoiFtuuaUoYQsKCqJBgwaameECDRs2ZPny5bz//vs888wzhIaGEhMTw7PPPounp6erwyuyf/9+MjIy6NSpk0YP3VhycjL5+fmaYikXxK3/MvzlL38pSqoul7CwMN56660Lek+7du0IDAw8Z51Dhw7xxRdf0LNnz6KyY8eOlar9nj17Fk3fTEpK4qOPPuK1114D4OjRo+zevRuALl26UKNGDQCaN2/Orl27+OWXX7j99tupXbs2AL1792br1q0X1D8REXd15MgRHnzwQQAWLlxIYGAggYGBRERElFi/sLDwrMlednY2n3/+OXv37j3jZtVeXl4EBAQUJXfW2qKkrfg6tgoVKtCwYUOCgoL4wx/+wE033VSUsDVs2LBcJQjiYIyhf//+REdHM2zYMF544QU+/PBD4uLiXPbl6IkTJ9iwYQNLlixhyZIlrFu3Dmstr732Gs8++6xLYpJLl5SUROXKlbn11ltdHYq4EbdO5sqLqlWrFr2uWLFi0a5h4Ei2wPE/3po1a15U8lm8fWstixYtOmOaR2pq6inTJz08PIq+Xdb0GxG5Vg0fPpwNGzawePFiGjVqdN76Hh4e+Pv74+/vT9u2bUusc+LECfbv33/WhO/LL7/EWktQUBAPP/wwQUFBRUlbYGDgFbmHmlx+N9xwAwsXLmTRokU8+eSTtG3blpEjRzJmzBgqV65c5ufPy8tj5cqVLFmyhGXLlvHTTz9hjKFt27bExMTw+eefM2bMGP74xz9y0003lXk8cvklJiZy2223XZHfJ7l6uHUyd6EjaJdDtWrVOHjw4FmP16lTh59//pmcnBx8fHz4+OOPueuuu6hevTqBgYEsWLCAnj17Yq0lIyOD0NDQM9ovPu3mdNHR0UydOpWpU6dijGHDhg3n/GYwIiKCp59+mpycHKpXr86CBQvOOKeIyNVo9uzZxMXF8dJLL3HvvfdetnYrVKhAnTp1qFOnDm3atLls7Yp7eOCBB+jUqRPPPvssEydOZNGiRcTFxREZGXlZz2OtZcuWLSxZsoSlS5fy2WefUVBQQM2aNYmOjqZr167cddddXH/99QDs3buX5s2bM3jwYFatWqXplm5m165dfPfddzz22GOuDkXcjP5Lv0C1atUiMjKSli1bMmLEiDOOe3p6MnbsWCIiIrj33ntP2eBk3rx5xMXFERoaSosWLfjf//53xvvvu+8+/vvf/xIWFsZnn312xvExY8aQn59ftFvZmDFjzhmvv78/MTExdOjQgTvuuIPWrVtfRK9FRNzLhg0bePLJJ+nSpQvjx493dThylfH19eWf//wniYmJHD16lFtvvZWnnnrqlE1rLsaRI0dYtmwZw4YNo3HjxjRv3pwRI0awf/9+nn32WT755BP279/P/Pnz6devX1EiBxAQEMDrr79OcnIys2bNutQuyhWWlJQE6JYEcuGMtdbVMZxVeHi4TUtLO6Vs8+bNNGvWzEURXd10bUXkavDrr78SHh7O8ePH+frrr4vWDIuUhUOHDvHiiy8ybdo06tevz8yZMy/oA/muXbtYunQpS5YsYfXq1Rw5cgRvb2+6dOlC165d6dq1K/Xr1y9VW9Za7rjjDtatW8emTZuoW7fuxXZLrrCePXvy5Zdfkp2dreUxcgZjzHprbXhJxzQyJyIiV40TJ07Qv39/du/ezYcffqhETsqcj48PU6ZM4bPPPqNy5cpER0czcOBADhw4UGL9/Px8PvnkE55//nlatmxJw4YNeeKJJ9i8eTODBg1i+fLl5OTk8NFHHzF06NBSJ3LgWCM/a9YsCgsLGTp0KOX5C3v5/woKCli5ciVRUVFK5OSCKZkTEZGrxquvvsrixYt544036NChg6vDkWtIZGQk6enpvPjii8ydO5fmzZuzaNEiAPbt28ecOXPo1asXtWvX5vbbb+ett97ihhtu4PXXX2fLli1s27aNKVOmEB0dfUkbYDRq1Ii//vWvLFmyhA8++OBydU/KUFpaGrm5uURHR7s6FHFDmmYpRXRtRcSdrVq1iqioKHr16sUHH3ygb7jFZdLT03nkkUfYsGEDN910E9u2bQMc69i7du3KPffcQ5cuXahevXqZnL+wsJBbbrmFrKwsNm3adMraOil/xo8fz/jx49m/fz+1atVydThSDp1rmqVbJnNNmzbVH+nL7OSuWUrmRMQd7d27l1atWuHn58fatWvx8fFxdUhyjSsoKOCNN95gxYoV3Hbbbdxzzz2EhYVdsc8vmzZtolWrVtx///3Mnz//ipxTLk5kZCT5+fmsXbvW1aFIOXVVrZmrXLkyOTk5mgd+GVlrycnJ0X1NRMQtHT9+nJ49e3LkyBH+85//KJGTcqFixYo8//zzrFixgtGjR9OqVasr+kV08+bNGTNmDPHx8SXuni3lQ25uLqmpqZpiKRfN7e4zV7duXfbs2cP+/ftdHcpVpXLlytr1SkTc0vPPP8+XX35JfHz8KbeDEbnWjRw5koULF/L4449z2223UbNmTVeHJKdZvXo1hYWFuiWBXDS3S+Y8PT0JDAx0dRgiIlIOxMfH8/bbb/P000/Tq1cvV4cjUq54enoye/Zs2rVrx7PPPktcXJyrQ5LTJCYmUq1aNdq3b+/qUMRNud00SxEREXCsoX700Uf5wx/+wOTJk10djki51Lp1a5577jlmz57NihUrXB2OFGOtJTExkc6dO+Pp6enqcMRNXVIyZ4x52hiTaYzZaIz5i7MszBjzlTEm3RiTZoxp5yw3xpgpxphtxpgMY0zry9EBERG59hw6dIgHHngAb29vPvzwQ7y8vFwdkki5NW7cOJo0acKQIUM4dOiQq8MRp6ysLHbt2qX1cnJJLjqZM8a0BAYD7YBQ4F5jTBAwGRhvrQ0Dxjp/BrgbCHI+hgDvXELcIiJyjbLWMnjwYL777jvmz59PQECAq0MSKdeqVKlCXFwcu3bt4qWXXnJ1OOKUlJQEoPVyckkuZWSuGfCVtfawtbYA+AS4H7DAyRun1AB+cL7+I/C+dfgKqGmM8b+E84uIyDVo2rRpzJ8/n7/+9a907tzZ1eGIuIVbbrmFJ598kqlTp/L555+7OhzBsV6ucePGNG7c2NWhiBu7lGQuE+hojKlljPEGugL1gL8AfzPGZAOvAS846wcA2cXev8dZJiIiUipffvkl//d//8d9993H888/7+pwRNzKK6+8Qr169Xj00Uc5evSoq8O5ph0/fpw1a9ZoVE4u2UUnc9bazcCrwApgOfANUAA8Djxjra0HPAOc3DqppJurnHGzOGPMEOdauzTdfkBERE76+eef6dmzJ/Xr12fOnDlUqKA9vEQuhI+PD7NmzeK7775jwoQJrg7nmvbFF1/w+++/a72cXLJL+ktorY2z1ra21nYEDgBZQH/gP84qC3CsqQPHSFy9Ym+vy/+fglm8zZnW2nBrbXjt2rUvJTwRkatOdnY2S5cu5bvvvuP48eOuDueKKSws5E9/+hM5OTksWrSI6667ztUhibilqKgoBgwYwKuvvsqGDRtcHc41KykpiYoVK9KpUydXhyJu7pLuM2eMud5a+7Mxpj7QA+gADAduA5KBzjgSPICPgGHGmPlABJBnrf3xUs4vInKtsNYSFxfHX/7yF37//XcAPDw8aNiwIU2aNKFJkyYEBQUVPderVw8PDw8XR335jB07ltWrVzN79mzCwsJcHY6IW3vjjTdYvnw5jzzyCGvXrtW2+C6QmJhIhw4dqF69+vkri5zDpd40fJExphaQDzxprf3VGDMYeNsYUxE4imPnSoClONbVbQMOAwMv8dwiIteEffv2MXjwYBYvXkynTp146aWX2Lt3L1lZWWzdupWtW7fy6aefFiV5AJUqVaJx48YlJno33HADxpQ08718+vjjj5k4cSKDBg1i4ED96RC5VNdddx3Tp0+nR48e/O1vf+PFF190dUjXlP379/P1119rqqtcFsbaM5atlRvh4eE2LS3N1WGIiLhMQkICQ4YM4bfffmPSpEk89dRTJa4Vs9by448/npLgnXy9ffv2U6Zk+vj4nJHgnXz29fW9kt07rx07dtCmTRsaNWrE559/TuXKlV0dkshVo2fPnnz00Ud88803NG3a1NXhXDM++OADHn74YdauXUvbtm1dHY64AWPMemtteInHlMyJiJQ/v/32G8888wyzZ8+mVatWzJ07lxYtWlxUW4WFhezevfuUBO/k6507d3LixImiurVq1SpK7Fq2bEn37t0JCgq6XN26IEeOHCEyMpLvv/+er7/+msDAQJfEIXK12rdvH82bN6dp06Z8+umnV9XU7PJswIABLF68mJ9//lnXXEpFyZyIiBv57LPP6NevH7t372bUqFGMGzcOLy+vMjnXsWPH+P77788YzcvKymLv3r0AtG7dmt69e9O7d28aNGhQJnGUZNCgQcTFxbF48WLuvffeK3ZekWvJ3Llz6devH2+//TZPPfWUq8O56llrCQgIoGPHjsyfP9/V4YibUDInIuIGjh07xrhx45g8eTKBgYG8//77REZGuiye7OxsFixYQHx8PGvXrgWgffv2PPTQQ/Ts2ZMbb7yxzM4dFxfHoEGDeOmll3j55ZfL7Dwi1zprLffccw+ffPIJmZmZGgEvY99++y0hISHExcXxyCOPuDoccRNK5kREyrnMzEz+/Oc/88033zB48GBef/11qlWr5uqwiuzYsYMPP/yQ+fPn880332CMoWPHjjz00EM88MADXM5byWzYsIEOHTpwyy23kJiYqGlIImUsOzub5s2b0759e5KSktxqgyR389prrzFixAiys7OpW7euq8MRN3GuZE53XBURcaETJ07w+uuv06ZNG3788Uc++ugjZs6cWa4SOYBGjRoxatQo0tPT2bx5M+PGjWPfvn08/vjj+Pv7Ex0dzezZs/n1118v6Ty//vprUXL473//W4mcyBVQr149Jk+ezMqVK/nnP//p6nCuaklJSTRv3lyJnFw2GpkTEXGRXbt20b9/fz755BO6d+/OzJkzL+sIV1mz1vLtt98yf/584uPj2bFjB56enkRHR/PQQw/RrVu3C0pKT5w4Qffu3Vm2bBmffvopHTp0KMPoRaS4EydO0KlTJ7755hs2bdpUptOor1WHDx/G19eXJ554gjfeeMPV4Ygb0ciciEg5Yq3l/fffJyQkhK+//prZs2fzn//8x60SOQBjDCEhIUycOJFt27axdu1annrqKdLT0/nzn//M9ddfz4MPPsiCBQs4fPjwedt79dVXWbx4MW+88YYSOZErrEKFCvzjH//g2LFjPPHEE5TnL/vd1WeffcaxY8eIiopydShyFVEyJyJyBf3yyy88+OCD9O/fn9DQUL755hsGDhzo9mtUjDG0bduW1157jV27dpGSksKgQYNISUmhV69eXH/99fTp04ePPvqIY8eOnfH+VatWMXr0aB566CGGDRvmgh6ISFBQEBMmTOB///sfH374oavDuWKuVOKamJhIpUqV6Nix4xU5n1wbNM1SROQKWbp0KY888ggHDhzg5Zdf5tlnn73q14QVFhbyySefEB8fz8KFCzlw4AA1atTg/vvvp3fv3nTp0oV9+/bRunVr/Pz8WLt2LT4+Pq4OW+SaVVBQQIcOHdi1axebNm3Cz8/P1SFdVkePHmXDhg2kpqby1VdfkZqayu7du6levTo1atSgZs2a1KhR45yvSzpWuXLl834p17JlS/z9/VmxYsUV6q1cLbSbpYiIC/3+++8899xzzJgxg+DgYObOnUtoaKirw7ri8vPzWbVqFfPnz+e///0vv/32G7Vq1aJmzZrs27ePdevW0bRpU1eHKXLN+/bbb2nTpg29evXiX//6l6vDuWjWWnbs2FGUtH311Vekp6eTn58PODZ+ad++PTfddBMHDx4kLy+PvLw8cnNzT3n922+/ceLEiXOey9PT85xJoLe3NxMnTuRvf/sbzz333JXovlxFlMyJiLjIV199Rd++fdm+fTvPPvssEyZMoHLlyq4Oy+WOHj1KYmIi8fHxrFy5kunTp/Pggw+6OiwRcYqJiWH8+PF8/PHH3HPPPa4Op1Ty8vJYu3ZtUfKWmprKL7/8AoC3tzdt27alffv2REREEBERUepNXqy1HDp0qMREr6TXJR07dOgQXl5efPvttzRp0qQsL4NchZTMiYhcYfn5+UyYMIG//vWv1K1bl/fff5/bbrvN1WGJiJTK8ePHadOmDb/++isbN26kRo0arg7pFAUFBWRmZp4yXXLz5s1Fx5s3b16UtLVv354WLVpQsWJFl8VbWFhIQUEBlSpVclkM4r7Olcy57rdaROQqtXnzZvr27cv69evp378/b7/9drn7ICQici5eXl7ExcXRoUMHRo4cyYwZM1wazw8//HDKdMm0tLSiXXL9/PyIiIigT58+RERE0LZtW2rWrOnSeE/n4eFx1a+RFtdQMicicpmcOHGCadOmMXLkSKpWrcqiRYv+H3t3HmdFdef//1V19633ja3ZUcCNXRQUiEuWGZwkbkGNJppoMsGoYzRm+WUmJlEnOqPJTBIVdEIMKvnpRBI1jiwxAgFcAAUU2bvZeu+++617q873j7p9u5tukIZuusHPU+tRVefWvfd003273nVOncMXvvCFvq6WEEIcl6lTp3LnnXfyyCOPcO211zJr1qxef0+lFLW1tWzdupV33nknF9727dsH2PemTZgwgZtvvjnXZXLEiBGn/IjAQhwv6WYphBA9YN++fXzlK19h2bJlfPazn2XhwoVUVFT0dbWEEOKExONxzj33XJRSvPfee/j9/h55XaUU1dXVfPDBB2zdupWtW7fmtpuamnLHDRs2LBfazj//fM477zy571h84kg3SyGE6EWNjY1Mnz6dxsZGHn/8cb72ta/JVWIhxGnB7/ezYMECZs2axQ9/+EMeU8a+zgAAIABJREFUeeSRbj3fNE12797dKbB9+OGHRKPR3HHFxcWMGzeOq6++mrFjxzJu3DjOOeccysvLe/pLEuK0ImFOCCFOgFKKW2+9lZqaGtasWcPkyV1eOBNCiFPWxRdfzG233cajjz7K1VdfzbRp0zodYxgG27dv7xDYPvjgA7Zt20YqlcodN3DgQMaNG8dXvvIVxo0blwtupaWlJ/NLEuK0Id0shRDiBCxatIgbb7yRBx98kHvvvbevqyOEEL0iHA4zfvx48vLy+O1vf8u2bds6BLcdO3Zgmmbu+GHDhjFu3LgOge3MM8/sdwOTCHEqkKkJhBCiF+zevZtzzz2XCRMmsGLFChmpTAhxWnvllVc6zDnncDgYNWpUh8A2duxYzjjjDAKBQB/WVIjTi9wzJ4QQPcw0TW644QY0TWPRokUS5IQQp73PfvazvPDCC5imybhx4xg1apTMmyZEH5MwJ4QQx+HBBx9k9erVPPPMMwwdOrSvqyOEECeFTLciRP+i93UFhBDiVPPWW2/xr//6r1x77bXMmzevr6sjhBBCiE8oCXNCCNENsViM66+/ngEDBvCrX/1KpiAQQgghRJ+RbpZCCNENd999N9u3b2f58uUUFhb2dXWEEEII8QkmLXNCCHGM/vznP/Ob3/yGu+++m9mzZ/d1dYQQQgjxCSdhTgghjkFNTQ1f/epXOffcc7n//vv7ujpCCCGEENLNUgghPo5SiptvvplwOMzKlStlKG4hhBBC9AsS5oQQ4mM8/vjjvPzyyzz22GOMHz++r6sjhBBCCAFIN0shhDiqbdu2cdddd3H55ZfzrW99q6+rI4QQQgiRI2FOCCGOIJ1Oc9111+H3+3nqqafQdfnIFEIIIUT/Id0shRDiCP7t3/6Nd955hxdeeIGBAwf2dXWEEEIIITqQy8xCCNGFVatW8cADD/DVr36VL3zhC31dHSGEEEKITk4ozGma9m1N0zZrmrZF07Q72pXP1zRtW7b839uV36dp2o7sY5efyHsLIURvaWlp4frrr2fYsGE8+uijfV0dIYQQQoguHXc3S03TzgK+BkwFDOAvmqa9DAwGrgDOUUqlNE0ryx4/DrgWGA8MBJZpmjZGKWWe4NcghBA96vbbb2ffvn28+eabhEKhvq6OEEIIIUSXTqRlbiywVikVV0plgDeAzwPfAB5USqUAlFK12eOvAJ5TSqWUUruBHdhBUAgh+o0lS5awaNEifvCDHzB9+vS+ro4QQgghxBGdSJjbDFykaVqxpml+4LPAEGAMMFPTtHWapr2hadqU7PGDgOp2z9+XLRNCiH5h37593HbbbUybNo3vf//7fV0dIYQQQoijOu5ulkqpDzRNewh4HYgCm4BM9jULgfOBKcASTdNGAFpXL3N4gaZpXwe+DlBZWXm81RNCiG6xLIubbroJwzD43e9+h8vl6usqCSGEEEIc1QkNgKKUWqiUmqiUughoBLZjt7i9qGzrAQsoyZYPaff0wcCBLl7zCaXUZKXU5NLS0hOpnhCnhc2bNzNlyhQmTJjAokWLMAyjr6t0Wnr00UdZvnw5jz76KKNHj+7r6gghhBBCfKwTHc2ydXCTSuALwLPAH4E52fIxgBuoB5YC12qa5tE0bTgwGlh/Iu8vxOlMKcXChQuZMmUK1dXVpNNpbrzxRoYPH86///u/09zc3NdVPG2899573HfffVxxxRXcfPPNfV0dIYQQQohjcqLzzL2gadpW4E/APyulmoCngBGapm0GngNuzLbSbQGWAFuBv2SPl5EshehCJBLhhhtu4JZbbuHCCy9k48aNvP/++7z66quMHTuWe++9l8rKSv7lX/6Fqqqqvq5utzU2NrJq1SoymUxfV4VkMsl1111HUVERCxYsQNO66hEuhBBCCNH/nGg3y5lKqXFKqXOVUsuzZYZS6nql1FnZLpgr2h3/U6XUSKXUGUqpV0+08kKcjt577z0mT57Ms88+y49//GNee+01Kioq0DSNT3/60yxbtox3332XuXPn8thjjzFixAiuu+463n333b6u+lHFYjGee+455s6dS0VFBTNnzuSMM87giSeeIJVK9Vm9vve977F582aefvppSkpK+qweQgghhBDddaItc0KIHqKU4oknnmDq1KlEIhGWL1/OD3/4QxwOR6djJ0yYwDPPPMOuXbv49re/zdKlS5k0aRKf+tSnePXVV1Gq09hCfSKdTvPKK69w/fXXU15ezpe+9CXeffddvv3tb7No0SJKSkq49dZbGT58OI888gjRaPSk1m/ZsmX853/+J9/61rf49Kc/fVLfWwghhBDiRGn95aSvK5MnT1Zvv/12X1dDiF4XDoe59dZbee6557j00kt55plnKCsrO+bnNzc38+STT/LYY4+xf/9+xo8fz913382XvvQlPB5PL9a8M8uyWL16Nc8++yxLliyhoaGBoqIirrrqKubNm8eMGTPQdfs6klKKlStX8rOf/Yzly5dTWFjI7bffzvz58ykuLu7VejY0NHDOOeeQn5/P22+/jd/v79X3E0IIIYQ4HpqmvaOUmtzlYxLmhOhbGzZs4Oqrr2bXrl3cf//9fPe7382Fne4yDIPnnnuOhx9+mPfff58BAwZw++23c+utt1JYWNjDNW+jlOK9995j8eLFPPvss1RXV+P3+7niiiuYN28el112GW63+6ivsX79eh544AH++Mc/EggEuPXWW7nrrrsYNKjnp6NUSnHVVVexdOlS1q5dy8SJE3v8PYQQQggheoKEOSH6IaUUv/nNb7jjjjsoKSnh2Wef5aKLLuqx13799df5+c9/zrJlywgEAtxyyy3ccccdDBs2rEfeA2DXrl08++yzLF68mK1bt+J0Orn88suZN28ec+fOJRgMdvs1t2zZwkMPPcTixYtxOBzceOON3HPPPYwaNarH6v3b3/6Wm266iQcffJB77723x15XCCGEEKKnSZgTop9paWnha1/7Gn/4wx/49Kc/zaJFi+iteRU3btzII488wnPPPZdrkbr77ruZNGnScb1eTU0NS5YsYfHixaxduxaAmTNnMm/ePK688soeG0Rk9+7dPPzwwyxcuJB0Os3VV1/NfffdxznnnHNCr7tr1y7OPfdcJk2axPLly7u8J1EIIYQQor+QMCdEP/LOO+9w9dVXs3fvXn7605/yne9857i7VXZHdXU1v/jFL3j88ceJRCLMmjWLu+++m8985jMf+/7hcJj//d//ZfHixSxbtgzLsjj33HOZN28e1157LZWVlb1W70OHDvHoo4/yq1/9ikgkwuc+9zm+973vccEFF3T7tTKZDLNmzWLz5s289957vVpvIYQQQoiecLQwJ6NZCnGSKKX45S9/yQUXXIBhGLzxxhvce++9JyXIAQwZMoSf//znVFdX8/Of/5zt27fzD//wD5x99tk89dRTnaYHSCaTvPjii1x55ZWUlZVx0003sX37du677z42b97Mxo0bueeee3o9EFVUVPDggw+yd+9e7r//ftatW8eFF17IxRdfzGuvvdatkTsffPBBVq9eza9+9SsJckIIIYQ45UnLnBAnQXNzMzfffDMvvvgin/vc5/jtb3/b66M1fhzDMFiyZAkPP/wwmzZtoqKigvnz5zNx4kSWLFnCCy+8QDgcpqysjGuuuYZ58+Yxbdq0Pp9UOxaLsWDBAh5++GH27dvHxIkTue+++/j85z9/1C6T69ev54ILLuDqq69m8eLFJ7HGQgghhBDHT7pZCtGH3nrrLa655hqqq6t54IEHuOuuu05aa9yxUEqxbNkyHn74Yf7v//4PgFAoxBe/+EXmzZvH7NmzcTqdfVzLzgzD4JlnnuHBBx9k+/btjBkzhu9+97tcd911nUbOjMViTJgwgWQyyXvvvUdBQUEf1VoIIYQQonskzAnRB5RSPPbYY9xzzz0MGDCA559/nvPPP7+vq3VU77//Pnv37uVTn/oUPp+vr6tzTEzT5MUXX+RnP/sZGzduZMiQIdx9993ccsstubnjbrvtNp544glWrFjBrFmz+rbCQgghhBDdIGFOiJOssbGRr371q7z00kvMnTuXp59+mqKior6u1mlNKcVrr73Gz372M958801KSkq44447GDp0KDfccAP33HMPDz30UF9XUwghhBCiWyTMCXESrV27lmuuuYaDBw/y0EMPcccdd/T5fWafNKtWreKBBx7glVdeAeC8885j7dq1eDyePq6ZEEIIIUT3yGiWQpwESikeeeQRZs6cia7rrFq1ijvvvFOCXB+YMWMGL7/8Mhs2bOCOO+5gyZIlEuSEEEIIcdrpf6MaCHEKamho4KabbuLPf/4zn//853nqqadkkI1+4LzzzuO8887r62oIIYQQQvQKaZkT4gStWbOGCRMm8Nprr/HYY4/xwgsvSJATQgghhBC9TsKcEMcpHA7z05/+lIsuugiXy8WaNWu4/fbbpVulEEIIIYQ4KaSbpRDdoJRizZo1LFy4kOeff554PM6VV17JggULyM/P7+vqCSGEEEKITxAJc0Icg9raWn73u9+xYMECPvzwQ4LBIPPmzePmm29m2rRp0honhBBCCCFOOglzQhyBaZq8/vrrLFiwgKVLl5JOp5k+fToLFy7k6quvJhgM9nUVhRBCCCHEJ5iEOSEOs2fPHp5++mmefvppqqurKSkpYf78+dx8882MGzeur6snhBBCCCEEIGFOCABSqRQvvfQSCxcu5PXXXwfgsssu45FHHmHu3LkyR5kQQgghhOh3JMyJT7QtW7awcOFCFi1aRENDA5WVlfzoRz/ipptuYujQoX1dPSGEEEIIIY5Iwpz4xIlGozz//PMsWLCAtWvX4nK5uOKKK7jlllu45JJLcDgcfV1FIYQQQgghPpaEOfGJoJRi3bp1LFy4kOeee45oNMrYsWN55JFHuOGGGygtLe3rKgohhBBCCNEtEubEaa2+vp5nnnmGBQsWsGXLFvx+P9deey233HIL559/vkwpIIQQQgghTlkS5sRpadmyZTz55JP88Y9/xDAMpk2bxpNPPsk111xDKBTq6+oJIYQQQghxwiTMidPOr3/9a775zW9SVFTEN7/5TW6++WbOOuusvq6WEEIIIYQQPUrCnDit7Nmzh+985ztceuml/OlPf5IpBYQQQgghxGlL7+sKCNFTlFJ8/etfR9M0nnzySQlyQgghhBDitCYtc+KkSWVMmuNpPE4dr8uBx6n36AAkTz/9NK+//jr//d//LXPECSGEEEKI056EOdGjlFLURVPsqouxqy7Gzroou+qi7KqPUd0Yx1Idj28Ndl5Xdu20tz0uR3b/sMezZbnHXTpep4NYUx2333En502ZzpTPXM2O2igjSwMyWqUQQgghhDhtSZgTxyWZNtnbEGdXXTQb2GLsrI+xqy5KJJnJHed16QwvCXLWoHyuOG8QZSEPRsYimTFJpi1SaZNk2t62y7LbaZOWuEFNF+WpjNWhLkop6l68n2QiSe15N3HV4+sAGDcgjxsvGMrccwfhc8tE4EIIIYQQ4vRyQmFO07RvA18DNOBJpdSj7R67G/g5UKqUqtfsJpLHgM8CceAmpdS7J/L+oncppaiLpNhZF2NXfZSdtfZ6V12MfU0dW9kG5HsZURrg8xMGMaIkwIjSICNKAwzM96HrPds6ppQilbFyAW/J88/y7R3rufMH93P1V75IMm1S1Rhn8boq7n3hfX72yodcPXkwN5w/jMpif4/WRQghhBBCiL6iKaU+/qiunqhpZwHPAVMBA/gL8A2l1HZN04YAC4AzgUnZMPdZYD52mJsGPKaUmna095g8ebJ6++23j6t+4tgZGSsX0nbW2l0id2Vb2yKptlY2n8vB8JIAI0oDjMyGtZGlQYaXBAh4+qaRt7a2lnHjxjFy5EjWrFmDw9HWAqeUYv3uRhat3ctfNh/CUorZZ5Tx5elDuWh0aY+HTCGEEEIIIXqapmnvKKUmd/XYiZyBjwXWKqXi2Td5A/g88O/AfwL3AC+1O/4KYJGy0+NaTdMKNE0boJQ6eAJ1ECcglsrw+3V7eeJvu6mPpnLlA/O9jCgN8oWJg3ItbCNLg1TkeftdAJo/fz6RSISnnnqqQ5AD0DSNaSOKmTaimEMtSRavr2LxuipuevothhX7uWH6MK6cNJh8n6uPai+EEEIIIcTxO5Ewtxn4qaZpxUACu8XtbU3T5gL7lVKbDht8YhBQ3W5/X7ZMwtxJFk6mWbRmDwtX7aYpnmbGqBKumjw219rmd58at1L+7//+L0uWLOEnP/kJ48ePP+qxFfle7rp0DN+aPYpXNx9k0d/3cv+ft/Lwa9v4pwmD+PL0oYwdkHeSai6EEEIIIcSJO+5ulgCapt0M/DMQBbZih7oLgMuUUi2apu0BJme7Wb4MPKCUWpV97nLgHqXUO4e95teBrwNUVlZO2rt373HXT3TUHDd4avUe/mf1bsLJDHPOLOOfZ49i0tDCvq5atzU2NjJ+/HgqKipYv349Llf3W9c272/hd3/fyx837ieVsZg6vIgbpw/jsvHluBwyBaMQQgghhOh7R+tmeUJh7rA3+RlQA3wfe4ATgMHAAez76v4N+KtS6tns8duAWUfrZin3zPWM+miKhat2s2jNHmKGyeXjy5k/ZzRnDcrv66odt5tuuolnnnmGt956iwkTJpzQazXHDZa8Xc3v1u6lujFBeZ6H66YN5dqpQygLeY/4PMM0aEw2krbSkP01UtmN3Fq17au2g454TFflrSoCFeR7Tt1/MyGEEEKI7jItk3gmTtAV/MROOdVrYU7TtDKlVK2maZXA/wHTlVJN7R7fQ1vL3OeAb9E2AMovlFJTj/b6EuZOTG04yeN/28Xv1+0llbH43NkD+NacUZxZcWp3J3z11Vf57Gc/y/e//31+8pOf9NjrmpZi+QcHeGrdZtZX7cHljnFOpc5ZlTpuT4zGZCP1iXoakg3UJ+qJGJEee+9joWs6Z5eczYWDLmTmoJmMKx6HrkkLohBCCCFOL8lMknUH17GiegV/rf4rjclGPA4PJb4Sin3FlPpKKfGV5Jb2+0W+Ilz66TUeQm+GuTeBYiAN3KWUWn7Y43toC3Ma8F/Ap7Fb7r6ilDpqUpMwd3z2Nyd4/I2dPPdWNaaluOK8gfzz7FGMLA32ddVOWDgcZvz48YRCITZs2IDH4/nY52SsDE3JplwIa0g0dAhljYm2kNacau7yNTTlpdBTxND8Ckr8xW0fGN4i3A63fQwdrxa1Xj1qLdfQOpW1rQ47tt1xGhoWFh81fcTq/avZXL8ZhaLQU8j0gdOZMWgG0wdOp8RXcqzfRiGEEEKIfqUl1cLf9v2NFVUrWH1gNYlMgqAryMxBMzmz+Eyak83UJeqoT9Tnlq7O2zQ0Cr2FRwx97csDrsAp0dp3UrpZ9gYJc91T1RDnV3/dwQvv7gPgixMH841ZIxlaHOjjmvWcb3zjGzzxxBOsWbOGadM6zmzRkmphU90mNtRuYGvDVuoSdTQkGmhKNrV1cWzH5/TZV3i8xbkrPcW+4g77fr2Q1R8leXbtIbbVRCjwu7hm8hCuP38oQ4r6Zs66xmQjfz/wd1bvX83qA6tpTDYCMLZoLDMGzeDCQRdyTuk5p91VKSGEEEKcXvZH97OyaiUrq1fyTs07mMqkzFfG7MrZzBkyhykVU3A5jnw+03rLS13cDnmt536Hh776RL19W8xhfE4fxd5iSv1toe/ms26mPFDem192t0mYO83trIvy3yt38NLGAzh0jWsmD+G2WSMZVODr66r1qJUrVzJnzhzuuusuHn74Yaoj1Wyo3cCG2g1srN3IzpadADg1J6MLR1MRqMiFshJvdp0Nb8W+YvyuYw9jSinW7W5k0d/38NqWGiylmHNGGV++YBgzR5X02ZQNlrL4sPFDVu9fzar9q9hUtwlTmQRdQc4fcD4XDrqQCwdeyIDggD6pnxBCCCFEK6UU25q2saJqBSuqVrCtaRsAowpGMXvIbOZUzumV20iUUoSNsB36kvXUxbsOfXWJOv7wj39gUHBQj77/iZIwd5radijCL1ds5+X3D+Jx6lw3bShfv2gE5XlHHrTjVNUcbubsc87GsAyuevwqNoc305BsACDkCnFu2blMKJvAhLIJnFVyFj5n7wXZgy0Jnl1XxeL1VdRHDSZWFvCvc8dzzuCCXnvPYxUxIqw7uI5V+1ex+sBqDsUOATAyf6Qd7AZdyKTySXgcH989VQghhBDiRGWsDO/WvMuK6hWsrFrJgdgBNDQmlE1gTuUcZg+ZTWVeZV9Xs1+TMHeKUuk0OJ2d+vJu3t/CL1ds57UtNQTcDm6YPoxbZg6nJHj6nKC37zK5oXYDrz76KrWv1TL8u8M5Y/IZTCibwHll5zGhbAIjC0b2yUAgqYzJHzfs5+evfURDLMVVkwbzncvPpDTUP/4dlFLsbtmdC3ZvH3obwzLwOrxMqZjChYMuZMagGQzNG9rXVRVCCCHEaSSejrP6wGpWVq3kjX1vEDbCeBwepg+YzpzKOVw0+CKKfcV9Xc1ThoS5U4SVTJLYsIHY+vXE179F4r330ABnaSnOsjKiwQI2Jly8l3ASDxUyZdIYPjPrbIqHDkLPyzslbuDsilLqqF0mS2pKWH7vcv7hun9g4eMLKfWX9nGNO4ok0/xyxQ6eWrUbn8vBty8ZzY0XDOt3c9UlMgnePvR2LtztDdtzOA4JDeHCgXawm1IxpVvdT4UQQgghAOoT9bxR/QYrqlew9sBaDMsg35PPxYMvZs6QOUwfOF3OMY6ThLl+ykomSWzcRHz9OmLr15Pc9J7dGudw4D1rPP5Jk9EcOgd3VrN/RzV6UwMlyTCBdKLTa2luN86ysnZLKa6yslwQbF30YN/P0ZE202xt3MrG2o258HakLpOjgqO4YMoFxONxNm/eTCgU6tO6H83Ouig//tNW3viojpGlAX70j+O5aEz/Cp7tVYerWX1gNav3r2bdoXUkMgmcupOzis9iRMEIhuUNs5f8YQwODZYBVYQQQpx20maa+kQ9+Z58fE5fn58jnWr2hvfm7n/bVLcJhWJQcFDu/rcJZRNw6s6+ruYpT8JcP2GlUnZ4W7eO+Pr1JDZtssObruMdP57AtKn4p07FN3Eils/Pqh31/PqvO1m/u5GSoIevXzSc66YNxWelydTVkamtza3TtbVkajuWWdFopzpoPl824LWGPTvkuQYPxjN6NO7KIWjOE/+lS2QS1MRqOBQ/ZK9jh6iJ17CzeSdbGraQMlMADA4OPmqXye9973s88MADvPbaa1x22WUnXK/eppRixYe13P/nrexpiHPJ2HJ++A9j+/2IooZpsKF2A6v3r2ZT3Sb2hvfmAjaAQ3MwODS4Q8BrXRd7i+WPnxBCiFNG2Aizat8qVlavZNX+VUTT9vmSW3dT4C2g0FPYtvYUUOjtel3gKcDrPPXHKVBKkcgkCBthWlIthI2wvaTCue2IEelU1pJq6TCidusIlGMKx8h5QQ+TMNdHrFSKxKZNxNe/RXzdOju8GYYd3saNwz91KoFpU/FNmoQjGCScTPPGtjpe31rDym21RJIZKvK83HrxCL40tRKvy9G994/FyNTV2UGvrq4t7LVb0nV1qHg89xzN7cY9ciSe0aPwjB6NZ9QoPKPH4Bo4AE23Q1ZrUKuJt4W0w9ctqZZO9Sn0FDI4NJhzS9ta3o7WZfKdd95h2rRpfPnLX+app57q1tfe11IZk6dW7eGXK7aTMRW3zBzOP88eRcBz6lydChth9rbsZU94D7tbdrMnvIc94T1UhatyYRzs1tSheUM7BLxhecMYmjf0tPgjJ4QQ4tR3IHqAldXZIfAPvUNGZSjyFjFryCzGF48nmo7SnGqmOdlMU6qJ5mQzzSl7u6tzmlY+py8X7DqFvvah0FtAnjsPDS03XZJSitx/2W37/+zj7crbP3b48zodqxSxdKzLUNa6nQtn2bKMyhzxa9TQCLlD5LnzyPPk2evsdusolAODA3vwX0scTsLcSWIZBslNm9ruedu4EZVK2eFt7Fj8U6finzoF/+TJOLLdBfc1xVm2tYZlH9SydlcDGUtRHHAz58wyPjW2nNlnluJxdi/EdZcZjWLs3Utq+3Zi2z4g+uFWMjt3odc25o4xPA5qyt3sKbbYWZSmuhSqSzWaA4CmUegppDxQToW/gvJAOeX+cioCFbl1mb+sWyf2hmEwZcoU6urq2LJlC4WFhb3wlfe+mnCSh179kBc37Kc8z8N9nxnLFecNPKWvWFnK4lDsEHta9rA7vJs9LXtyQa919EywP/wHBAZ0CnrD84ZTHijvk0FrhBBCfDIopdjauJW/Vv+VlVUrc0PgD88fzuwhs5k9ZDZnl5yNQ//4c6yMlSFshDsEvaZUkx32kp3XLakWIulIb3+J3eLQHG2BrItQdvh2+/AWdAXlb3YfkzDXSyzDIPn++8TWrbPD24YNdnjTtHbhbSr+yZNw5OXZz7EUmw+0sGxrDa9/UMsHB8MAjCwNcMm4ci4dW86EykIcvTxvWeugI+sPrWf9wfXsatnFofihDleffEnF4Ho4s9nHqCYPQ2otSg4l8IaTbS+UF8I7ZjS+MWfYLXnZ1jxHwYkN0//jH/+YH/3oR7z00kvMnTv3hF6rP3hnbxP/unQL7+9vYfLQQv517njOGpTf19XqcfF0nKpIlR3uWkNedh1Lx3LHeR1ehuQNocRbQpGviCKvvRR7i3PbreW9Oc2EODlM0yIVy5CMpknGDJLRDImoQTKWJhlNYyQy+EJuQsVe8op9hIq9hIq8OFxy8iCEOHZpM836Q+tZWb2Sv1b/lZp4Dbqmc17pecweMptZQ2YxLH/YSatLi9HSIeSFjTAKhdb6n2avgdz24WVA5+O1zmWHlwdcgQ4hLeAKnNIXkj/pJMz1oN1vvcX8r3+d2cEgFzQ1U5DJgKbhOfNMAlOn4p82Ff+kSTjy207Uk2mTv+9q4PWtNSy2T7IeAAAgAElEQVT/oIaacApdg8lDi7hkXBmXjC1nRGmw1+t+KHaIdQfX2QHu0PpcK0qZr4yxxWM7tKQdrUUt09BAavsOUtu3k9qRXW/fjhVpuwrlLCvLdtEcjWeMHfJcw0Zgue0TNF3XjvihsnnzZiZOnMiVV17J4sWLe+8bcpJZluL/f2cfD/3lQxrjBtdOGcLdl51B8Wk0pcSRKKWoT9TnumzuDe+lKlJFY7KRxkQjjclG4pl4l8/1O/0dwl2HwJctby0r8BQc9SqrMk2UYaBSKayUgTJS7fZTqGxZ23b2mNbjU6m2xw3DPiaVwjLs491DhhCYOYPA1Knogf59n+TxMjNWLoQlo2kS0XTbfrt1+3IjceTuO06XjtvnJBFNo6x2f480COS5CWXDXV6xt9thL2JEOnUBb+0ulFtcoQ77QVfwmK7UCyH6h5ZUC2/uf5O/Vv+VVftXEUvH8Dl9XDDwAmYNmcVFgy+iyFvU19UU4oRImOtBrz25gC9/8xvUZjLomsa08eO54qqr+KdrruGMM87IHdcYM1jxYS3Lttbwt+11xA0Tv9vBRaNLuXRcObPPLKMo4O7VutYn6nnr0Fu51reqSBVg37s2pWIKUyumMnXAVIblDevW1RqlFOmUSTKWzl1tT0QN4ocaie2rJX6omURTzH7cAMPhI+MKkHG2H45W4dAsHDo4nBpOlwOHx4nm0vj/Ft5KXdMB/vt7L1JcVILDpeNwajhcDpxO3d536ThdOo7WfWd2v91jHr8Lb8CFN+DE6e4/J2ctiTS/WL6d367Zg8/t4M5LxnDD9KH9biqD3qLSaTINDWTqG9rClGGQTESJxpqIxZqJxpuIx8MkEmGSiQhGIoqRiJNOxTFTScxUEkdG4TLBZYIzAy5T4TI1PJYDr6XjNjVcpobTVDjSFnrGRM9YJ1x/ze1G83jQPB50t7tt3+UitWsXKh5Hc7nwTZpEcOYMAjNm4hkzut9fETXTFpHGJOGGBJGGJOH6JNGmZKdwlk6aR3wNp8eBL+DCG8wu2W1fu+3D167s76ZlWsRaDCINCcINSbsODUki2fpEGlOdwp4n5MCZZ2EGUyR9YcKeBuqdBzmg7WWv2kHEDHeoX/t7VY4m4AocMey1dj0KuoId9kPuEEG3XSYjvwrRu/ZH97Oyym59e6fGvv+t2FvMrCGzmD1kNtMGTJN7tsVpRcJcD1KZDJlwmPd272bp0qUsXbqUjRs3AjB85ChGTZlFeuBE9jgHoTQH5XkeLhlbziXjypk+orjbg5h0R0uqhbcPvZ1redvRvAOAoCvI5IrJdnirmMrowtHomo5SCjNtkUpkSMUzHa+ux9KkcvuZXFnrYmWO/HPj9jpyJ2sevxO3lsZpRHBGGqGlgUw0QSaeIJMwMNMWpu7C0l1YupOl2//G7zf/iW9d9A2mjbkY5fRgOd1YmhMLB6bSMC0Ny+zez63TpeMNuvBkw50d8lr320Jf6wmmHQSd6L0YsHbURvi3P23lze31jC4L8qN/HM+M0SW99n69TVkWZlNT2+A6tbVkatoNtlNbQ6a2DrOhAY7jc0dzuezglF2Uy4nl1Mk4NTJOjbQDUg6LlG6R1DPEtQxxzSCGQUxLk3YqDKdG2glpB7m14bS3LZcDp9ePy+fH5Q3g8YXw+oJ4/CF8/nz8/jwC/gKCnuxJu8teB93B3Mm9y9RIbthA9M03ib25itRHHwF2S3Vg5gyCM2cSmD69Q8v9yWKaFtHGVC6s2YEtkQtNsZYU7XOOrmsECjz4Qp1DmC/owht0278zQXf2MSfOHvp8i6fjuda0Q7FD9qi4kRoaGsNEG1IYzQpXzE8oVZRbgqlCdDq+vxbI4CnQCRZ7KS4NUVZeiD/owXSnSTuSGM4kST1GTIsSsyJEjAhRI5obHCBiRIikO5ZF01EsdfSLAj6nD6/Di67pOHQHDs1hb7df6/a6ddE1PXd8+2OdmrND+eGv43a4KfYWU+yzlxJfSW7/VOqi3HpPUkuqBZfuIt+TT9DV91PpiP5BKcXWhq2sqF7ByuqVbG/aDsDI/JHMrrTvfzur5Cy5r0uctiTM9RLTUmyoauIPb2zgjy8tpWrDmySr3gcrgz+vgE9d9mm+fM0Xufzyy3t8fjQzbdEUCbNh3ybe27+ZbYe2c7C5FpfpIajyGOodwSDPEMqcAwioIJmkHdqMpEk6mSGVyJBOmFjWkf/9dYfW8Sp6wIWnXRBqX54LQQEnjm4EICuRyJ3sf/DuBmbM/xazR4/micsuaxuBs6bGvhexHYWGVlCEXjYArbQcraQcragErbAYLb8IK5BHOq2RTFqkkopU0rKXlCKVVHZ5dvtovwJuN7g9Gl6Phtuj4ckt4MmWBwtcFJT68OV70LMtNnbg8KC7XeByHfGERCnF61tr+MnLH1DVGOfy8eX84HPjGFLUfybVVEphRaNtoaympuPIqDU1pOtqydTVQzrd6fmOoiKc5eWdpsNwlhSjebxoHndbC9eRlqN8D4+FpSxi6RhRI5o7OY+mo7mT905l6WiuvPWYWDr2sa06HYa19hQwMOFl9Ecxhmypo3jzfpyxJErXYPwYvBdOp/DiOeSfMxHdceIhyDItos2pXKtaa4tWuMFubYs1pTr8rGsaBAu9bV0YS3xtXRlLfATy3Sd8MSNjZYilY7nvaev38vDvc9gIUxuvzQW4sBHu9FpF3iIqAhW5QZYO3y5xl2JErWNv2TuMw6nj9jlw+5y4vU7cPicenxO3N1uWLXf5dHCZZFxpDEeClCNBUo8R16NEVYRo9mcmaSYxlYlpmZjKxFJWbm0pK1d++GOm1XZMRmXaHrOsLo9Nmskuv19gtzAWe7MBz1fcMfR5S9rCn68Yj6PnunsbptFhIIjmVHOHpSXV0uGxplQTEaPzYBEOzUGeO498Tz55njzy3fnke7KLO1uW3W5fHnKHPlFdZWPpGLXxWpqSTbgdbrwOLz6XD5/Tl7uwcCqFYqUUKTNFykzxfv37uRa42kQtuqYzoWxCbgCTyrzKvq6uECeFhLkeFDcyvLm9nte31rDiw1oaYwZOXeP8EcVcMraMaYN9bF7/JkuXLuXll1+mqakJt9vNnDlzmDt3Lv/4j//I4MGDc6+nlMJImsRbUsRbDOJhg1hLinjY3k7FMxiJDEY2gCXiSdJJC836+JOs1pMTl7ftpKTDdvuTFb+zUzBzeRwn7Q+AZVlcfPHFbN68ma1btzJgwIDcY0oprHA4GyTaT61Q0za/Xk0Nmfp6MI/cBawrCjAdXtKugL04/aRdATKuIGlngLTLny0PkMkdEyDj6hy2nOko/ngt/kStvY7X4E/U4kvW43RquZDXoWuex4PmdqFcbvZFM3zUZGDoDkYOLGLs0GLcPl/2WDda+xP+w35vO/0et98//Fe8i2OVgpTpIJFxYSVT+JurUHWHci1sKtF5ono9FGo3QX15h8npXeWtga0Ezd273YlPlmMJhOFUOHdy2jqsdevJq2ZZjDoA5+2yOG+XYsRB0IGwH7aN8rF3bCG1Zw3EXVpmD3PtLaDAWUhIyydPK8CvQvhVAK/yY8X1Dt0hIw1JYk2pjhdnNAjkuwkWewkWeQgVewgWewgUugll15rD/v3ODYkNHYa6NpVJzIjlvt6uvu6uAnDrcxKZzj83h3PrboLuIGX+sg7hrPW+3dZtt+PEfo5au3G23sOXyn6uGgnT/ozN7WdIJdouehnZi2BGMtP5d+kwmgZunxOX15H9bHXi8jrtkOhtFwy9hx2Te9w+1uV2oHVjEKy0laYx0UhDsoH6RD0NiQYakg00JLL77cqPFPxCrhDFPvve09aA19rKV+IrId+TTzwd7zBUe+sw7oeHtaP9u/ucPgo9heR78tt+zrPDurcGsoyVoSXVkpvvqnW7xciWpcIfO1JgyB1qC3ztwl5rOGzdzoXF7Kh9/Sn4JDNJ6hJ11MXrqE3UUhurpS5RR228tq08Xks8E0e3HLhNH6aeJq0boHX8YW0Ndu0Xr9N7xDK/03/E47xOL6ZlkjJTJDPJ3DppJrtVlsqkSJrJTmUpM9XhwpnP6ePCgRcyu3I2Fw26iALviQ2wJsSpSMJcD1q5rZavPP0WIa+TOWfag5dcfEYped6O90hYpkWkKcHK5W/w51f+zP8tf5Xq/XsAGFU5loljZnLW0OmUeodidtFlUXdq+EJulCdDXIvQbDVSb9WQ0hOknSkK8/IZUjSQEaXDGFU2gmDAl7163HZScCqNBPdf//VfzJ8/n//5n//hxhtvPK7XUKaJ2dhIuqYWs7FdV772f5hz21oXj7UWdXH8Yc+zLIWR1kgmLCLNaVqaMrS0mITDinBMI5Fqf1VY4XcaBB0JgnqMoAoTUGEC6Sa86RZIJVGGgWWkSCdSNDVHSSdSeFSGACa6mYHMkQeQ+NjvCxqGO4+Up4Ckp4CUp5CUpyC3JD0FGJ4CrHb3+WjKJGQ1UeiOUZxvUTLAS3FlAZ4B2da1sjJ0f/9pPewvWu8nTadM0tkAkE6aJBMG4WiMlliEaCxOLJYg2RJFHWhCr4/jiFgo3JgODymPD8PpQWkedI5+71XM1ULE02gv3obcdtjTQNTThKV37+JGd3kdXrurafb+saArmLtvLLftCuXuQevQPTX7nBMNaSeLsux/WyMX8sxc+DO62E8lMvbxrWEwGxYzxjHct6mB2+PIBsP2IbDt893ls8Ogx++isMJP4YAAHt/Hz2NpmAaNycYug1777ZZIPcHaCIPq7VGNy5sVhhPiXoh7NGIesII++6JOKA93fgGegmJ8BcWE8kooaDepcvvA1lP/3hkrQ8SIdAh5h4e/3LZhB8DW7aN1k3Xprk5DtbcPe7nh29vttz5+rPdopc009Yl6ahO1uUCWC2nxOjuoRetJxy186SDedBBfOoQvEySYyadAlRAyC/FngrgNP3rKBanDuhi7FLgscFkoVwbTmcF0psk4DNKOFIYjSUpPkNLjJLQYCS1GXIsSI0Jci+aOSTtSZHQj9/exu1y6C6/Ti9fhxePw4HW2rVvLPE4PXoe3rczpsY9xeKnMq2TagGk92nJ8OjHTdo+MWHOSaFPKXppTxFtSeAMu8kp9FJT6ySv1kV/qw+X55LRYn24kzPWgWDzNus21jMrzkYqms61pdqtaLGzk9hPRdIeruEopapqr2HpgLe/v/Ts79m1GKUVhYQlnT5jI6ClnUnpWCWFPI7UcpNY8SLPRjKUsNDTOLDqTaQOmMaViCpPKJxFwnT4j5e3evZuzzz6bmTNn8sorr/Sbq6InwkhmaKlN0Fwbp7mm42K0G0DC4dTJL/NRUO6noMxPQbmPgvIAu1Mpfvr6h2w5GGHqsCJ+9LkzGFdxWFddTcPM2K0NseYU0WYj+6GeattvShEPpzj83EV3agQLPAQLvQQKPAQLPAQKPQQLPSgL6qoi1O4NU1cVIRW3g6Tu0CgeFKR0aIiyyhBlQ/MoGhjA4Tx1LhocL6UUyWj6sO9v23brfir+8a03AGjg8jhwe+zWcpfHgdNKooWboOEQWv0hHOkEToeJc1ApalgJqZElRPMhrkVIOuNooYx9wkbbUNTQNrx163bbW3bxONpRj9E1vXNYy4a0oDsoA30cB8u0ci196aTZoeWvq+10a4vhYcd2NRBNoMBD0QA72BVWBCgaYC/e4JH/naxEAmP3blI7d5LauRNj505SO3ZiVFXlejooTcMqKUA3FVosDinj6F+k04kjGETPy8MRCqHnhXAEs+tQXsf93DF5uefowd67V661hb051dzlZMqtwa+ryZajRvSoXa3durvL+bqcupP6WAMtzVGikQRG1MKXsQOaNx3Alw7iz+QRMgvwZ0K4DT+OdNehV9M1fEFX9l5WN/6QC2/IXrt9Lsy0hZGyfz7SyQxGFxeXjJT92DFdWABA4SCNQxk4zBQOM4VOBl2zcOgK3QEOh5YdzEzH6XLgcrtweV243E6cXhcOjwun143TZy8uvxdnwIPT78UV8uP0eXC5HblBzBxOe9TrT7K0YRLLhrNYU5Joc4poo70fbUoSa06RiHS+tcHtdeDP95CIGqRiHS8E+/Pc5GeDXWvAyy/1k1/qwxNw9vn5Vzpl2r3TWlLEmu2eaq3nOMpS9rlSdiks9+M+hgtY3aEMg/SBA7gGD0Zz9uxrnygJcz2oamsDf/rFpg5lmgPcQR09YKF8aQxPgpQnStTZQouzngatllrtAAetagzse78y4QyRTREiGyNE3o+gDIXD62DQ5EGcMeMMJlw8gcHlgxlXNI7JFZPJ95x+c5KBfZJ86aWXsm7dOrZs2UJl5end/10pRSKSprkmRnNNguaaOE01cVpq47TUJToM7OLxO8kEHHwYTVCjTM4anM/E0jysWCYXHhLhzidVTo+DUKHnsJDmbdsu8OANHts9aEopwvXJDuGudm8kN9S87tQoGRSkdGgeZZUhSoeG7IB3Co3MaaYtYi0dw1m06bDQ1pLqPOiPBv6Qm0BB2/faG3ThyraeuDyOXNdmt9eR3be7131cNzozEiH2978Te3MV0VWryBw8CIB75EiCM2bgnzYVz5gxuAYORNNPne+16DmtLYXxsEHToRiNB2M0HYznttufqPtCLgrLvOT50gQzTfhbqvEe+BB2bSWzb19bLwaHA/fQoXhGjsQ9aiSekaPwjByBe/hwdG9bq5NlGFiRCGY4nF1HsCJhzEik4344ux9p2zcjEVS862lIchwOHAUF9lJor52Fhdmy7Lp1P/u4Iz+/138XTMskmo7mAl6L0dJlIAwbYRLNadz7isk/OIjCloG4M0cYjEYDb8BhBzS/E1/AgdfnwOfT8Ph0vF4Nn9e+R9vnUbicCs0y7WlWTBNME5UxwTJRmQxWNIoZiWJFW7/v2e1otG07ErX/XWJxTIeHjNOL6fBgOrxkHF5Mp8deOzyYLj+WL4TpDWJ5ApguP6bTi6l0TEvDVBqW0jHRsXBgaQ57wLIeaIHVlP2q9mLiwELXFLpmrx2aQtdB1xUOHXQde4Rsh33xUXdouZBpL3rb4rKDo+5y4HQ7ceYF8Q6rxOF2oun2c/XWtUOzy3Q9t9/6mJbd7m4IMpKZ3N8a++9NkkhTyg5vTSmizclOQQzAE3ASLPASLGz7ex4s9BAs8Ob22wecVDxNS10it4TbbceaO45F4PY5uwh69hLI93Sr6/fh0oZJrDnb8NGS6ridvc0o1pzqcLG7lcOpEyiwf54iDckOd4r4890Utgt4BeV+CisChIq9R7wYYEZjpKurMKqqc2ujuop0VTXpgwfBshj5l1dxDxt23F9vb5Aw14Pe2b2JRf/3Ag1aDbXsp1GvI+WMd+qC4HV4c/cfFHoLO86LddjiVV5W/211bnTMgwcPous6M2bM4I477uCf/umf+vxqSW9ZsGABX/va1/j1r3/Nbbfd1tfV6VOWaRFuSNJcE6elNkFTtiWvqSZGvNkObQlN4Q65GDIoREGxz/5AP+zD3O3t3Xsd7YCXoHZvhLq9EWqrwtRVRXMBz+HUKR4czIW7sqEhCgecvICnLEUmbWW7O2ay96QanVrTWreT0c5XNp0uPfeHMdBuab/vz3eflK9JKYWxcyfRN1cRe/NN4m+9hcoONKP5fHhGjszO6TjKXo8ahXPgwNP2M0McndncTHLHDpq27KF+Zy1NB+O0RDQiWj6xwADMdiNcujDI92coKPVSMqKIkjMHUTQkn2Chp3c/Q9LpbLjoIgi2hDFbWjCbmzGbmjqsM83NXQ6yBICu2618hV2HPWen8kI0lwuVTGbnkExiJZPZuSMP307Z61QKlds2ss+1j7OSSZqNAIesCmqcQwi7ygDwpRooCu/Ak2rGZURwGWFcyRbcRgS3EcWZiaMdU3N+92luN3ooZLd4hkLooaDdIhoMtm2HQjhCQfRg9vFQ9vGgva15u38PoVIKyzAwo3HS0QRGJE4mliATS5GO2SNZZxIG6UQKM5kmk0pjpjJkUhkyRsYe5TptkkkrTAssS8NS9tpEx1Kt8U5H5eJeuyCpObB0J5buRGlO1EkYDMcOe23hruswaH8fYy1Gl3Nv+kKu7N9zb4feMu33XT04zVLGMGmp7xjwWrcjDckO92A7XDp5JR0DXl6pj/wSH5pOrhWt9W9tLGy3rMWzLWtdfb0Op44/300g30OgwI0/30MgP3uBNM+Dv8B+zONvazE00xYtdYncuVFzTZymQ/a5UmsvIvu1NUIhjZA7RSDTjD92EG/tTtxVW9HrD3SsR2EhrsohuIdU4q4cgmtIJaE5s/tk1OmjkTDXgz5q+ohH3n7kyOHMV0ShpxB/FwNkHAvLsnj33XdZunQpzz//PB999BGXXHIJv/jFLxg7dmwPfzV9a//+/YwbN46JEyeyfPlydGlhOKJ0yqS6Mc4v3tjBS5sOkOd18c+zR/Ll6cN6dbqLY6UsRUtdomMLXlUk1xXM4dIp6RDw8iis8GNZ7e4xa78kTTJG57L0MZRlDPOoXR1zfzCPEtTa//Hob6x4nOSH20jt2E5qxw6MHTtIbd9Bpq4ud4zu9+Me1RbuPKNH4xk9Cmd5eb/9ukTXlGWhEgnMWAwVj2PF41ixWG6daWzC2LWT1M5dpHbuxKyvzz1X8/nwjBiBZ9RI3CNG4h45ErN8KBEtn6baJE0H21r0krG2kOTyOrLdNO0um0UVAQoHBMgr9p7Q1fkTpZTCisUxm5swm5rtdfuw19Ra3jEIKuNjuoV2U+sAVpYvQHPBGOryzqTON4KkIwRKUag1MMBZywBvE3m+DLrHbXfZcuhoDiea0wG6o23tcHQsc9hlrWt724mWbXZqe6y1zJl7niMbxPRQCP00GXyqu5RS2RbLDCqTwUwZmEkDM5Umk0xjGpncunVJ1TaS/PBDEts+Il1bj9J08AdxjRyFe+QoXMNHopeWoSywTIVlKZSlsEzL3s+WWZa9rVr3Tcs+NnsMgL+gXYtaoYdAgZdAgbvHpnbpCZZpEWlM2eGuPpHrPRSut8Pe0brodghp+W78BdmQlu8hkN91SOsulU6TPngw17qW2ltNtPoQzTUJwmFFzFlI3F9O3F9OwlvSIdB7nBny83UKKgIUDy+hqLKQgnI/eSXeXp2O6kRJmDtFZTIZfvOb3/DDH/6QaDTK/Pnz+dGPfkR+P7tacDyUUsydO5fly5fz/vvvM3LkyL6u0iljy4EW/v0v23jjozoG5Hu589IxfHHiYBz97P6C1oBXuzdMbZXdildXFSGdOr5BORwuHZfH7qLoynZbbF2cXZS1P86fZ1/5C+R7Ttt7/MzmZlI7dtjL9h25bbOhIXeMHgx2aMWzA99onGWlEvJ6iFKqrQtiLI4Vj3VeHxbIrC5CmhWPo2J2+cfRg8HOXSNHjsI1cMAxdz1MRIxssIvReDBubx+KEW9pC0JOj4OiAQGKBwUoHhi014OC+EL9NzQopeww3NREprm5Q9hTmQy614Pm9qB5PeheL5rHa4cvrxfNk51uxuvNTTuTNDT2bmliz3v1VH3QSCZl4vQ4qBxXxLCzSxh6VjH+vP77/RAfL11TQ3z9emLr1hFf/xbpqioAHPn5+KdOwT91mt3VffToT+TnplKKeNjIteQpBcFsb5WevBhqxWIY1dUY1dWk23WFNKqrSR840GH0cs3jwTVkcIfWNXs9BEfFACItZu62lvZjGLS/51B3aOSX2mMYzLhqNHkl/WueTglzp7i6ujp+8IMf8OSTT1JaWsqDDz7IjTfeeEq3ZP3+97/n+uuv5z/+4z+48847+7o6p6Q1O+t56NUP2bSvhTHlQb5z+ZlcMrasX/9xUZaiuTZO7d4IzbVxnK0BrTWQtd5n5nF2DGluvV9fMevPMk1NpLa3a8X7yN42m5tzx+h5eW2teKNG4RkzGs+oUTiKi/v1z9PJoAyDTFMzZlOjHQgaG+1A0NiI2dxEprHJbgVqbCSTbTE6ptFndR3d77eXQKDrdet24CiP5eXhLO29MJ6MpWk6FKfxQJTGAzEaDkRp2B/r0D3Zl+emeKAd8IqyAa9oYKBHu4T1FaUUTQfj7Hm/nt2b6jm0uwWUPdjM8HNKGHZuCYPGFPSrVhXRs9IHDhBbv574uvXE168nvX8/YM+h6p8yBf+0qQSmTcM9YsQn/vOyO5RSZOrqSFdXZ1vYWoNbFca+fR0uRIIdpl2VlbiHDOnYLbKy0v4MPI5z4mQs3aGrZmvgu+KO8wjk968RVCXMnSbeffdd5s+fz5o1a5g6dSq//OUvmTp1al9Xq9tqamoYN24cY8aMYdWqVTh6YMLkTyqlFK9uPsTDr21jV32MyUMLufczZzJlWFFfV030Y0opzIaGTq14qR07sFpacsfpfn/bpPe6bk/RoWtoWuu2bj/W+rieHSlTz45GoGEfewzPtSeId9ktIK52czH+v/buPD7uut73+Os7W5JJJvvaLN33tIW2FCo7lIIcioLAVY8XBJWjgsrxnus53qOPy70PlXM8Vz1cF1QuKHpQFEFtVUDgAGXpQim0TSnd0+xJ0zTJzCSzf+8f398sWVratOlMks/z8ZjH7zff328mv6S//mbev+/mcpmaksQk8lmJuReHzNnoHKUssb8zUR6L19LEQ1kipA0LZdbzmM93wr+jvaAAe3Gx6YdVVISjuMgM0FFcjD3fYwWx0YPaWPoiZYr4nfme1ni4M0Gvp81PJGw1v1JQUJpjgl1KTV5BWU7G35iJRmO0H+ijcUc3h3d103/UzJtXVudhxtJSZi4tpbR2/EbcFJkt1NLKwJYtDGzdgn/LViIdHQDYS0vJTam5c82YMeXPkVgoRLi1dWRga24i1NyCDgSSO9tsOCsrk4GttjZRu+aqrcWen5++XyQDSJibRLTWPP7443zlK1+hvb2dO++8kwceeICKiop0H9opu/XWW1m/fj3vvPPOpOsHmC7haIwnt7Xw7y/so8sbZM3Ccr5y3QLmVaLySGkAACAASURBVHje/8VCWOJ3Sk0/vP2EWlshGrMmlo9BTJuRD3UMHYuZvomxmHmuNdYoBYDpT2K2DXttLGaep742GkOHw+hQyDyCQWLhkBmAIqVsPKmsLCuYFeIosgJacZEZOKOoCHuRtS0e3goKMm7o6nSLxTT9RwcTtXc9rT6Otfnp6xpIDpjpsFFU5aakOm9IU013gSutX3yDA2GadvdweGc3TbuPERyIYHcoKuYUUrmoiJK5BdjznIQiMfOIxhLrwWHPQ5GoKbPKw1Ftmt9qa0APHX9u1s1/jeT2+Hp8u7aW0djQfYdvj6V8nzPTjljrKnXakeTvbKYgSd0nWW7eI3X/lPcjfm/GlMXfJ/7cZj1Xiefm55uKE2Wej7qf9T4267gUuJ0OinOdFLpdFOe6KHQ7Kc51UeR2ZUZ/ca0JNzcnmmQObNlCpKsLAEd5Oe5VqxI1d87a2kkZ7qJ9fYSaW5IjRLY0J0aIjLR3JEfLxfTjddXUpAS2Glzx9WnTUFO0n+epkDA3CXm9Xr7xjW/wve99j5ycHO6//37uvfdenM7Mnvfpqaee4pZbbuFb3/oWX/3qV9N9OJPOQCjCz15v5McvH8QfinDz8hr+/pp5VBdmVttvIU6X1hrCYWKhoQFPh0JmZMFEebwsiA6Fk/uFTZnN7TYBrbjY1KJZNWoqJ2dSftHKBJFQlOMdAxxrNbV4x9r8HGv1DemPl5XrMOFumpkXz2a3YXMo7PbkcPB2R3zdNuS53WHKlE0R1jF84Ri+UARvKEJ/KEpfMEx/IExvIMLxQJi+QJjjA2G0N0xpf4wKn6Y8oLGhGFCaQ84oBxxRGp0xwmd4SigFTpvNqrhWicBjtynruQkzNpXcPnxfW8p2pZQZht9at6VsV1iJTJOYD0/r5HhQWuuU9ZRxooaVg3l9Yn3YeyTLkuEyvk9quNQ6+TOT5ebdk6H05K8PRU880EaO0z4k4BW6XRS7k8GvKNdFkdtJkdusF7td5Ixz01+tNaHGxkSTTP/WrYlBiRxVVeSctwybK8vc0IpaN7Ni1oAt8bL4Da+Tbk8pO9F262aa+feN/4FHKdPJG22JcyFeFt932PPkezBihFl7ScmQppCpgc1eWirX2TGSMDeJ7du3j/vuu49nnnmGhQsX8uCDD3LNNdec+hvEohDygdMN9vENgseOHWPx4sVUV1ezefPmjA+eE9lxf4gfvnSAX2w6AgruWD2dz18xh6JcueslxEQVCEc50OVjX6eXfZ0+9nd6ae8LkOOyk5vlIC/LTq7LYa07kmXW83hZcrvZP12TMwd84WS4a/MlavJGmxD9bNKAVmCzvv6Ecu0Eyl1EKrKxlbpwOe1kOey4HDZcdptZOmxkDXs+cps9uc0qc4xhDjKRFI7G6B0Ic3wgxHF/yCwHwvT448+tbQPJ532DJ5i+Ashy2BI1e0W5VtBzu6gsyGZJdQFLawoodJ+9z0mtNaFDh6wBVbYS2L3bhCabzfTxsh6jryuUzX7CsqHbFdjsyabs8e1KYbV3TzR9Rw0rTzSBH1ZmVdcmmsYPKbPWUdgLC01wq6vDVVODLTf3rP39RJKEuckkGoGQF0J+CPog5EMH+vnz8y9z3wM/5WBzJzddtoTv3H0NM4udI/ZNLOPrEdMXAFceTL8YZl0Bsy6H8kVD22OMUVdXF3/+859Zv349zz//PMFgkG3btrFs2bIzfm/x/lqOD/C95/fz9Nst5GU5+Ozls7nr4pnjfndSCDF2gXCUQ0f97O/ysrfDCm5dXpp6ks0VnXbFrNI8qotyCISj+IMRfMEI/qC1Hopwqh/vbpd9RMAbGvzsuF0ONBCNxYjENNGoNstYfBkb+jx6gvLU/aPmeeq2cDSGdzBCMBzFjglcdsAG2LXCBmTbbBRkOyjMcuLJspPvcuBxOch12slz2XHb7biddnLsNnIcdrLtCpfNhtLWsPLRGLGIJrcwixlLSygoG9tUQiIzRaIxegfD9A6E6PGHhwS94wMhevwha1uI3oEwPQNmGTe9xM3SmkKWWuGuvrqA3CxpUi3SS8Lc2dS+A54d1jxwxN9wlL/pWPcJD0LQCmQhH0QCI19nCUQ039sU4huvBolp+MplHv5xTSXuvHzIyjOBzZWXXE+U5ULPYTj8Chw7YN4st9yEullXwMzLobD2hD936K+g2b17Nxs2bGDDhg1s3rwZrTU1NTXceOONfOITn2D16tWn9F7i7Hmvo59/e3YvL77XRUV+Fl+6eh63razBkeEDEQgxmYUiMQ53+9nb6WV/p5d9nV72d/poPOYnPl+vw6aYUZrL/AoPcyvymFfhYV5FHtNLcnGe5P+v1prBcHRowAtGRoY+q8wfiuAbdT+zDFgDmzhsCrtNJZd229DniaVVbj9BeeL1CrvNNuT1eVkOinJdFOSYmpNCt9N6mGZzOU671HaJs6pvMMzu1j52tPSxs6WXnS19tPaam902BXPK81hSXciy2gKW1hSysMpDlmPy3xQNhKNWGB5ZAxoPxQU5ThZXF7B4Wj5zyz24Jun0P+kmYe5sat8Bz/3z++836geNOv19nG4TthIBzGOeJ8KYZ8T2lq5evvK1+/n1r39NXV0d3/nOd/jIRz5yah9+vc0m1B162Tz81kTExbOtWrsrYOalkFOUeEkoFGLjxo2JAHf48GEAVq5cybp167jxxhtZtmyZfPhmgK2He/iXZ/awvamXWWW5/Pe187muvlL+bYQYR+FojMZuP/s6TRPJ/V2mtq2x20/ESm02BTNKc5lXbsLa3AoP8yo8zCzNzYgvR7GYTgx2IcRU0O0Lsquljx1WuNvZ0ku3z/TzdNoVCyrzWVJTwLIaE/Dmludl9A3SwVA0pWbS1Egmmq6OaLJq1gdCJ27y7MlyUJjrpMcXwm/t57LbmF/pob46n0XTCqifls/CqvyMGKxmopMwN0Vt3LiRL3zhC+zcuZOrrrqKBx98kPr6+lN/A62ha08y2B153dQOougpWMwzXRWs33mcZ1/bTn9/P9nZ2axZs4Z169Zxww03MG3atHH6zcSZ0Frz/LudfPu5vRzo8rGstpB/um4Bq2eXpPvQhJgUdrX08dLerkRN26FuH+Go+axVCqYXu62wZmra5pZ7mFWWK194hMhgWmva+gLsaukdUoPnDZh5JbOdNuqnFVgBr5ClNQXMKMk9a31SozGNLxChPxDGG4jgDYTpt5belGW/Vd47rEYtXrs+mvxshzVgjDVojDVgzPCyeF/DwhxX4iZTLKZpPOanoa2f3a197G7rp6GtL9F01W5TzCnLY/G0fBZXm4C3aFo+nmwZN+F0SJibwiKRCA8//DBf+9rX6Ovr45577uH++++nqKjo/V88zL49u9nw+E9Yv+FPvN7QSDSmqchVrFuQxbpLlrDmb27GvWgtVC0zHXFFRotEYzy9vZXvPr+Pjv4AV8wv4yvXLmDRtKk9l4sQY/VuWz/ffX4fL+zpBKC2OId55Z4hwW12WZ70WRVikogHmV2tfexoNgGvoa0vEZw82Q5rYJVCltUUML/SQygaS4axwdRQduKA5g2Y5s7vx+WwkZ/twJNtmiYXu61RPlOmd4iHs/gIoIVu50mbbI+F1prW3kF2WwGvoa2fhtY+urzJKWZmlLitcFdAfXU+i6cVUCyDtJ2QhDnBsWPH+PrXv85PfvITiouLeeCBB7jzzjtPOmF3JBLhjTfeSDSf3Lt3LwBLly41zSevu5qVpUFsjRtNzV3XbvPC7EKYeZnV5+5KKJ51VgZTEeMjEI7y2BuN/PClA3iDEW5YOo27L53FkpqCdB+aEBPCgS4v33thP3/e2Y4n28Hdl87i9g/MoCBH7jwLMdVEojH2d/kSNXc7W/p4r6M/UTt/Ik67wpPtTIQxT7bDepj1/GHLRHlOct9M78fX5Q0kA15rP7vb+2juGUxsn1aQneh/Z0JeARX5WafcvDsWM32F/aEIg6Eo/mCUgVAEfyjKYCiSeD4QiuIPRRkIpmwLJbf98OPLmZZhUzpJmBMJb7/9Nl/84hd57bXXWLFiBd///veHDEjS39/Pc889x/r16/nLX/5CT08PTqeTK6+8knXr1rFu3TqmT58++pv7uuDwRjj0Ehx6BfqaTXlBrQl2M6+A2VdCbun4/6LitPUNhHnolYP8clMj/lCUVTOL+dQlM1mzsAJ7moYuFyKTHTnm58EX9vOHd1rJcdq565KZfPqSWRS4JcQJIZIC4SjvdXg52OUj22kfEcLys51kOWxTsk9q70CId62mmbutGrxD3f7EmICleS4WTSugutCM3JsIY0GzHLBCmD8YZTB8etOauF1mpF6zNKP6ul12Hrh5CTVFmTXK7biFOaXUl4DPYEbteFhr/e9KqX8D1gEh4CBwp9a619r/q8CngCjwRa31cyd7fwlz40NrzRNPPME//MM/0NbWxu23386KFSvYsGEDr7zyCuFwmJKSEq6//npuvPFG1q5dS37+aTa90xp6DiX72x3eCIFesDmh/iOw+h6oWjoev544Q/2BML/Z2szP32iktXeQ6SVu7vzADG5dWSvDMwsBtPYO8v0X9/PkWy047Yo7Vs/g7y6fLU2EhBDiLPAHI7zX0U9Dqwl3DW39HPUGRgSvHGcygMWDWW6WnRyXg9yUoJablRrazD7ZDnva5tgci3EJc0qpeuAJYBUmuD0LfA6YCfyn1jqilPpXAK31PyqlFgG/tvafBrwAzNNanzBGS5gbXz6fj29+85t897vfJRQKsWDBgsTok6tXrz5pE8zTFouakUB3/ha2/wLCftMUc/W9MOcaM5mlyCiRaIxnd3fwyGuHebupF0+2g4+tquOOD8ygOsOaHwhxLnT2B/jhSwd4YqtpdfDxC+v4/JWzKfdkp/nIhBBCTGbjFeZuBa7VWn/aev51IKi1/nbKPjcBt2it/9aqlUNr/YC17Tngfq31phP9DAlz50ZrayuBQIDZs2efmx842AvbH4MtP4H+ViiZC6s/D8s+Bk4JCZloe9NxHnntMM82dADwwfpKPnXJTM6vO/2BdISYaLp9QX788kF+ufkI0ZjmtgtquffKORnXp0IIIcTkNF5hbiHwR2A1MAi8CGzTWn8hZZ8NwG+01v+hlPoBsFlr/R/WtkeAZ7TWvxv2vncDdwPU1dWtOHLkyJiOT0wA0TC8+0d44/vQ/g64S2Dlp2DVZyCvPN1HJ0bRcnyAx95o5ImtzXiDEZbXFfLpS2exdlFFRs+vI8RY9A6EePjVQ/zs9UYC4Sg3nV/Dl66eS11JZvWlEEIIMbmNZ5+5TwH3AD7gXWBQa/331rZ/BlYCN2uttVLqh8CmYWHuL1rrp070/lIzN0VoDUfegE0/hL1/AbsTlt4GF90DFYvSfXRiFL5ghCe3NfOz1xtp6hmgujCHOy+ewW0X1JIvc8eICc4bCPPIa4d55NXD+EJmhNf71sxldlleug9NCCHEFHRORrNUSn0LaNFa/0gpdQfwWeBqrfWAtV2aWYr3130AtjwEbz8OkUGYfZXpVzf7KpneIANFY2YC8kdfO8zWxh7yshzctrKWOy+eQW2x1F6IiWUgFOGxN47wk40H6R0Ic+3iCv7+mnksqJS5F4UQQqTPeNbMlWutu5RSdcBfMU0uLwS+C1yutT6asu9i4FckB0B5EZgrA6CIUQ30wFs/gy0/BV8HlC00I2AuvQ0cWek+OjGKXS19PPLaIf60s52Y1qxdVMmnLp3JyulFU3K4ZTFxBMJRHt/SxEMvH6DbF+LK+WV8+Zr5MteiEEKIjDCeYe5VoAQIA1/WWr+olDoAZAHHrN02a60/a+3/z8BdQAS4T2v9zMneX8KcIBKChqdg0w+gswFyy02fupV3yXx1GaqjL8Bjmxr51ZYm+gbDLKsp4K5LZnL9kiqc0q9OZJBQJMZvtjXzg//cT2d/kIvnlPDla+azYroM7COEECJzyKThYuLT2sxVt+kHsP+v4MiGZR81/erK5qX76MQoBkIRnnqrhUdfb+Rwt5+qgmxuv2g6n5jpw3PgD9D4umk6a3eBzWH6SibWXdZzp5mb0O4Cu1Vucw5bd47+2vg2R7Y5R3LkC7owItEYT29v5cEX99PaO8jK6UV8ee08PjBbbhAJIYTIPBLmxORydC9s/hHseAIiAZh7rWmCOfMy6VeXgWIxzea3ttGy8Zec1/cC82ytRLETrlpBdo7bjGoaDUM0BLGIWcbLYlZ5NF4eAsZ4zSqZCzUXQM1KsyxfZEKhmDKiMc2GHW08+OJ+Dnf7WVpTwH9bO5/L5pZKU+AzobWZS1RHhy1jp1+uY5A/DfKr5XouhDDXF18XDPakXCfiSz302qFTry962L6xYfuOVmat198COYXp/s2HkDAnJid/N7z5CLz5MPiPQuUSM1jK4pvB4Ur30Yn+dtj9NOz6HbRtB8BfuYpnuZhvNy+gK+bh6gXl3HnxTD4wu+TUv0zHoicIf8Oex9dDA9CxE1rfguatMNBt3sfphmnLrXBnBTxP5Tj9MUQ6hSIx1u9o48evHORAl48FlR7+29r5rFlYPrFDXG8zHH0PIsHkzY7Ew/o/ERmlLBpMWU/dJzzK+wz7/xWLjQxhY73BcjLZhVBRDxWLrUc9lC8ElwysJMSkEouCt91cz/qaofeIWe9tMs/7WsyN+3Pp3m1QOvfc/sz3IWFOTG7hAOx60kxtcHQP5FXChXfDijvBXZzuo5taBnrM3IENT0Hja4CGqmXmLlf9zVBQA0CXN8B/bDrC41uaOOYPMb/Cw12XzOBD51WT7bSP3/FpbT4oWrZBy5vm0b7T1AACFNQmg13NBVC5FJzZ43c8Ylz5gxGeeLOZR149RFtfgAWVHu69ag7X11dhs02wEBeLmn7DTVugeTM0bYb+1lN/vd0F9qxkk+R4c2TH8LKUbXZXcrvNaZox2+ygbNbSPmx5FsqVguON0LnbPLrehZDP+iUUlMxOhrt40CucLrV4QmSqaNhcq3qbhoa03ibz6G81N4pSuUuhsA4Ka83ncuF0M05C4jphS7l+qFHKrPX4NWVEWXzf0crskFOccS13JMyJqUFrOPiiCXUH/9PUvJz3cbjwc1A6J91HN3kFfWZ+wF2/M3//WMQ0aVxyC9R/5KR3twLhKOt3tPHoa4d5r8NLca6Lj6+q47+unk5F/jkKUeGAqblLBLxt0NdkttmcpsY3Hu5qVkLRDPnimOF6/CEee6ORxzY10jsQZtXMYj53xWyumFc2cWrigj5o3WbCW9Mmc16GvGabZxrUXQh1q80NB5d79DDmyEr2JZ0ov/dwsZi5AdPZYAU8a9lzKLmPy5NSgxcPeosgy5O+4xZiqggHTO1ZX9Pogc3bbpouJijTCqawzgpqtdZ6nbWskRr4UUiYE1NP527Tr27nb81doXnXwerPw4xLJ+6XmkwSCcL+56Hhd7D3WTMnYH6NqX1bcov5gnkaf2etNZsP9fDo64d5YU8ndqW4YWkVd10yk6U1aWi37u1IhrvWt8wjPGC2uUutYLfCLKcth2yZhywTtPYO8vDGQ/zmzWYGw1HWLKzgc1fMYsX0CVBD399matuat5hlxy7TjBFlAkrthVB3kXkU1Mp1LOiDrj0pIc96BPuS+xTNGNlUs2imuRsvhDg1g71WWGu2mkI2pTSJbAZ/19D9ld30eR1Ss1aXDG351TLF1BhImBNTl6/L6lf3/0xfKelXN3bRCDRuhF1PwZ4N5kuTuxQWf9g0o6y98Kx8STpyzM/P32jkyW0t+IIRVk4v4q5LZrJ2UQWOdE1tEI2YJrzxmruWN6F7n7VRmb48NSthxmUw52pp3nuO7ev08uNXDrL+nTYAPnReNZ+9fBZzKzK0ZiYWM+dTk9VcsnmzuYMN4Mgx51KtVfNWszLjOuJnLK3NF8zUGryOBug5mKwZcLrN4EfxcFdZb57L31hMRbGYGXOgL7U2rXnoMtg/9DX2LFN7NiSopdSyeaZlXBPFyUDCnBDhQVNLt/lHZsCAvMrkfHXyxfvEtDaDhjT8Dnb/3lz0s/JhwQ2w5CMw84pxu2h7A2Ge3NbCz99opKlngOrCHG5fPZ2PXlBHgds5Lj/ztAweh9btQ/vfBXpNm/vaC2HetaZGuGyB1KKMk7eO9PDQywd5YU8XOU47H11Vy6cvnUV1YU66D22o0IAZBKhpk9XnbWuyBim3PFnjVnsRVC01TSTF2RMaMNf9RA1eg3kMHk/uU1CXrMGrrDdBr3iW6T8jxESV6K/WPHrNWl+rGRApVVZBSlBLXVq1a+5Sqd1OAwlzQsQl+tX9yCwdOXDex+Ciz2fcyEVpo7X5orPrd9DwtLnwO7JNOKm/BeauPaeDgkRjmhf3dPKz1xvZdOgYOU47t6yo4ZMXz2B2Wd45O473FYtB29uw71nz6NhpygvrTKibdy1Mv0QGVDlDWmte3nuUh14+yNbGHgrdTj75gRncsXoGRblprG2PxcxAHcF+CHqhe7/VZHITtO9IdvAvW5Csdau70DT7k7B/7mlt+vJ0NCTDXedu8++mo2YfR46pdY+Hu3jYkzkrxenQ2tw48HaYG6KJEZfjU/BEUp5Hhk7JEwuPsi3lNYlRnIe9XyRofp63nRGjzeZVjqxZSwS2GsguSMufSZychDkhRtO1x5qv7jfmztRUn6+u55BpQrnrSejea9q9z77K9IGbf31G9At7t62fn71+mD++00YoGuOK+WXcdfFMLs3EecL6Ws0E9/ueg0Mvm36FzlyYfaUJdnPXylQIpyESjfGnne38+JWDvNfhZVpBNp++dBYfXVWL23UGtcNaQ8ifDGFBLwT6kuunVN6fHJwklT0LqleY0FZ7EdSukpYAmS4cSKnFs0JeR4OZ4youv8YKePHBVurNKJtSize1pIY0bzv4Os3S2zny+fDar9Nhc5jBuOzO5Miydmt02cRzhzXYkbVuc0JexcgatoIa6a82QUmYE+JkfEdhm9Wvzn/UfDCvvseMxDjZL3q+LlP7tutJM3IewPSLze++6MOQW5Le4zuBbl+QX21p4pebj3DUG2RueR53XjyTm86vJseVgV+owoNw+FWr1u456G8x5dPOT9baVS6b/E1XYlEzfYWv03Sa9x01S3+3uZs8ZDJXM+FrJBrl0FEve9v7GAhGKMyxs6Ail9qibGzolIlhUx4w6nuhY2Ygm+EhbMhIayfgzDWjI2bnm2WWxzQ5zsofvbygxkzLMdmvIVOB1uYLe+du6NyVbK7ZvS9Z4+rINrV4FYuhYkmyFk/C+8STCGntVlDrAF9Hcj1RdoKQllUAngpzsy6v0izjj9wyc67YHMlRZ4eEMufIbZl2o1KkhYQ5IU5FfL66zT8ycxvlVcAFVr+6DA01YxLoh/f+ZH7XQy+bL7KVS2DJrSbEWXPBTQTBSJQ/72zn0dcP09DaT6HbycdW1XH76ulUFWRYv6k4rc35FQ92zVsBbT7056014W7m5ZCVQU1ITyYWhYFj5saAv8ssE+tHreB21JQNdI8enGzWMPrKZn1xUcSUjUBEMxDWRDU47HZys5xkuRwoZQNUyrxBtuRrh68zrMzpTgauEcHMM3o4c3mkQ78YKRKEo3uHTpvQ0WDO87j8ahPqZ19lBkcqnpW+453qwoGh16XEtcla93Ymg1s0NPL12QUjw9loz2VYfTEOJMwJcTq0hkMvmfnqDrxg7qIt+6jpV1c2P91HNzbxqQR2PWlCRCRgJuFccqt5lC9I9xGeEa01244c59HXDvPc7g6UUly/pIo7L57B8roM79/i7zbn2b5n4cCLpvmePQtmXJKstSuafu6OJxZNqbnqtYLZ0VHC2vsENEe2Gdwjr8xaWo8hZRVmPSs/cfe5oy/AI68d4ldbmvCHolw5v4zPXTGHC2YUZV5TWiGG09r8v0itwWveYiZCBxPm5qyB2VfDzEvBlZvWw53wIiFzLUq9Nvk6R16vfF1Dp61IlVNkrkeeSvBUWbVqVeb6FH8uIU2kmYQ5Icaq6z2rX90TpjnFnGvMfHWzrsz8pg+xKBx53YziuWe96e+TW2amZVhyqxnyPNN/hzFo7hngl5uP8OutTXgDEc6rLeQjy6u5dnEl5edqIvKxiobNgBn7njPh7tgBU162MDk6Zs0Fo9cSRcMj+3Kl9u8KDC8fbT8vhP0nPr5EQIsHszJrvSK5Hg9rKQHtVBzo8vHTjQf5/dutxDSsW1rF310+m4VV6e+rKcQZO3bQ3Kw58AI0vmqa+9pdZiCcOWvMo3zhpLwmn7ZYzNwk8nakhLLO0WvUUkckTZVVYK5DiWtTRfK6lVqWWybTFIkJQcKcEGfK3w3bHoWtPzUfIuWLTairvyWzRifUGtrfsUaifMq0+XflwcJ1ZiCTcZxKINP4gxGe2t7CLzYd4UCXD6VgRV0R19VXcl19JTVFE+Aua/cB2P+cCXdHXjf9c3KKzLxYId/QEBYJvP/7KduwJoXDH/lDt2XnD/3Sk+U5q182I9EYr+7v5ok3m/jru5247Db+ywW1fObSWdQWT4B/HyHGIhwwN20OvmgCXte7ptwzzTTFnLMGZl0++UbNjIRSmjTG+6F1mqWvK9kPzdeVHFE0lSsvJZjFr0spAS31JpMzQ5vZCzFGEuaEOFsiQROUNv0QunabD42aVaYZXOF0M8RvfP1c9nk6dtAc164n4dh+0wdp7loT4OZdN+Wbh+zv9PJMQwfPNHSwp91MgLqkuoDr6iv5YH0lszJpioMTCfTBwZdMjd3xI0NDWHb+SUJaSpnTnfY7/1prdrf18/T2VtbvaKXbF6LI7eRvL5zOJy+eQWmeDBgippi+VivYvQAHXzbNAZXN1MLPWWMCXtX5mTtAUtA3ekAbHtRSRwRNUOZzNN6UMbGsTAlpVlCTJqliCpMwJ8TZprUZPOStn5sO8L1HTLOZVO6SkQGvaDoUzjCDjJxpjZ63E3Y/bZpRtm0HlOlnteRWWHTj5Lur3PQrCgAAC8JJREFUe5YcOebnWSvYvdPcC8D8Cg/XWsFuQaVH+maNg46+AH98p5Wnt7eyt9OL0664ekEFNy+v5or55bgcGfpFVYhzKRqB1rdMsDvwgpm7Em0+T2ZfZfW3u8qEm/EU9Fl90Y6m9JntTvZPSw1qId/I19tdySDmqRx9Ga/xnyKtRYQ4ExLmhBhvWpsR/Y4fgd5Ga9lkQt7xI9DXPHJ0LE9VSsAbFvryq0f/gAv0wZ4Npgbu8EZrJMqlsPQ20xeuoPqc/LqTRVvvIM/tNsHuzcYetIYZJW6uq6/iuvpKltUUSLA7AwOhCM/t7uDp7a28dqAbrWF5XSE3L6/hhqVVFLqlr4oQJ+XvNjXyB14wtXf+o6a8cmmyr13tKjOM/cnEYqZ/mb9rWDiLPz+aDGr+7pE3J+OyC1KaOp4kqOUUpb0VgBCTiYQ5IdItFjP911IDXq8V+I4fMfOOpY4IqOym9i414HXsMn2nokEommmNRHnLxB1hM8Mc9QZ5/t1OnmloZ9PBY0RimmkF2VaNXRUrphdht8mXk/cTjWk2HzrGU9tbeLahg4FQlJqiHG4+v5qbltcws1SaSgkxJrGYGSXzwAumr13zFtOP1uUxfexmXGL6zvq7kyPOJh7do/dDU3YTznLLrFFmUx7x/me5paZvWm6pzJsoRJpImBMi00XD0NcyNOAlQl+Tac6SW2bmgVtyK1SvkLue46hvIMwLezp5pqGDjfuPEorEKM3LYu3iCj5YX8lFs0pw2qVZYKr9nV6efruVP7zdSntfAE+Wg79ZWsXNy2tYOb0ImwRhIc6uQJ9poREPd33NptyZa4JXIoylhrN4MLOeZxdmbl88IUSChDkhJrpwwDSjsdnTfSRTji8Y4aX3uni2oYOX9nYxEIpS6HayZmEF1y2u5JK5pWQ7p+a/S7cvyIYdbTy9vZVdrX3YbYrL55Vx8/Jq1iysmLJ/FyHOufj8dll5MlCIEJOQhDkhhDgLAuEoG/cd5dmGDp7f04k3ECEvy8GVC8r5YH0lF88upcD9Pn1XJrhAOMqLe7p4ensLr+w7SiSmqa/O56bza7hx2TTKPNIMSwghhDibJMwJIcRZForE2HToGM82tPPX3Z0c85sBbqoLc1hY5WFhVT6LqvJZWJVPXbF7Qjcz1Fqz7chxnt7eyp92tuENRKjIz+LD51dz8/k1zK/0pPsQhRBCiElLwpwQQoyjSDTGW0eO83ZzL3va+3m3rZ9D3X6iMXN9zXXZWVCVz8IqD4uqClhY5WFBZT45rsxuhtjY7ef3b7fy+7dbaeoZIMdp54P1ldy8vIbVs0tkQBghhBDiHJAwJ4QQ51ggHGVfpzcR7va0m3VvMAKATcGM0txE7d2iqnwWTcun3JM17tMhhKMxurxBOvoCdPUH6OgP0NkfpLM/QEdfgE5vgM6+AP5QFKXgA7NLuPn8Gq6rryQ3S+aEEkIIIc6lk4U5+VQWQohxkO20s7SmkKU1hYkyrTUtxwd5NxHw+nmnuZc/7WxP7FOc67ICnodF00zQm12Wd0qjZ2qtOT4QHhLIUoNa/NHtC414rdOuKPdkU5GfxYJKD5fNLWN6iZvr6iupKsg5O38UIYQQQpxVEuaEEOIcUUpRW+ymttjNtYsrE+V9g2Heazfhbk+7l3fb+3ls0xFCETP3oMtuY25FXqIGrzjXZQUzqzbNCmld/UFC0diIn1uS66Ii3wS1pTUFlHuyqSzIpjI/m/L8LCrzsylyuyZ0vz4hhBBiKpJmlkIIkYEi0RiHuv2JZprvWmEvtVbN7bJTmZ+dCGoVBdlUWEGtIj+Livxsyj3ZuBwyj5QQQggxUUkzSyGEmGAcdhvzKjzMq/DwofOqE+Vd3gD9g2Y0ybwsx7j3rxNCCCFE5pIwJ4QQE0i5J5tymQlACCGEEIC0vRFCCCGEEEKICUjCnBBCCCGEEEJMQGcU5pRSX1JKNSildiul7rPKipVSzyul9lvLIqtcKaX+r1LqgFJqp1Jq+dn4BYQQQgghhBBiKhpzmFNK1QOfAVYBy4AblFJzgX8CXtRazwVetJ4DfBCYaz3uBh46g+MWQgghhBBCiCntTGrmFgKbtdYDWusI8ApwE/Ah4DFrn8eAD1vrHwJ+oY3NQKFSquoMfr4QQgghhBBCTFlnEuYagMuUUiVKKTdwPVALVGit2wGsZbm1fzXQnPL6FqtMCCGEEEIIIcRpGvPUBFrrPUqpfwWeB3zADiBykpeMNhnSiBnLlVJ3Y5phUldXN9bDE0IIIYQQQohJ7YwGQNFaP6K1Xq61vgzoAfYDnfHmk9ayy9q9BVNzF1cDtI3ynj/VWq/UWq8sKys7k8MTQgghhBBCiEnrTEezLLeWdcDNwK+B9cAd1i53AH+01tcDt1ujWl4E9MWbYwohhBBCCCGEOD1jbmZpeUopVQKEgXu01seVUv8C/FYp9SmgCbjV2vcvmH51B4AB4M4z/NlCCCGEEEIIMWUprUd0W8sYSqmjwJF0H8coSoHudB+EyBhyPog4ORdEnJwLIk7OBREn54JIdTrnw3St9aj9zzI6zGUqpdQ2rfXKdB+HyAxyPog4ORdEnJwLIk7OBREn54JIdbbOhzPqMyeEEEIIIYQQIj0kzAkhhBBCCCHEBCRhbmx+mu4DEBlFzgcRJ+eCiJNzQcTJuSDi5FwQqc7K+SB95oQQQgghhBBiApKaOSGEEEIIIYSYgCTMnSal1HVKqb1KqQNKqX9K9/GI9FFKNSqldiml3lFKbUv38YhzSyn1qFKqSynVkFJWrJR6Xim131oWpfMYxblxgnPhfqVUq3V9eEcpdX06j1GcG0qpWqXUS0qpPUqp3UqpL1nlcm2YYk5yLsi1YYpRSmUrpbYqpXZY58L/sspnKqW2WNeF3yilXGN6f2lmeeqUUnZgH3AN0AK8CXxMa/1uWg9MpIVSqhFYqbWWOWOmIKXUZYAP+IXWut4q+zbQo7X+F+tmT5HW+h/TeZxi/J3gXLgf8Gmt/086j02cW0qpKqBKa71dKeUB3gI+DHwSuTZMKSc5F25Drg1TilJKAblaa59Sygm8BnwJ+DLwtNb6CaXUj4EdWuuHTvf9pWbu9KwCDmitD2mtQ8ATwIfSfExCiDTQWm8EeoYVfwh4zFp/DPPBLSa5E5wLYgrSWrdrrbdb615gD1CNXBumnJOcC2KK0YbPeuq0Hhq4CvidVT7m64KEudNTDTSnPG9B/mNOZRr4q1LqLaXU3ek+GJERKrTW7WA+yIHyNB+PSK97lVI7rWaY0qxuilFKzQDOB7Yg14Ypbdi5AHJtmHKUUnal1DtAF/A8cBDo1VpHrF3GnCkkzJ0eNUqZtFOdui7WWi8HPgjcYzW1EkIIgIeA2cB5QDvwnfQejjiXlFJ5wFPAfVrr/nQfj0ifUc4FuTZMQVrrqNb6PKAG09Jv4Wi7jeW9JcydnhagNuV5DdCWpmMRaaa1brOWXcDvMf85xdTWafWTiPeX6Erz8Yg00Vp3Wh/eMeBh5PowZVh9Yp4CHtdaP20Vy7VhChrtXJBrw9Smte4FXgYuAgqVUg5r05gzhYS50/MmMNcafcYFfBRYn+ZjEmmglMq1OjSjlMoF1gINJ3+VmALWA3dY63cAf0zjsYg0in9xt9yEXB+mBGugg0eAPVrr76ZskmvDFHOic0GuDVOPUqpMKVVorecAazB9KF8CbrF2G/N1QUazPE3WELL/DtiBR7XW30zzIYk0UErNwtTGATiAX8m5MLUopX4NXAGUAp3A/wT+APwWqAOagFu11jIwxiR3gnPhCkwzKg00An8X7zMlJi+l1CXAq8AuIGYV/w9MXym5NkwhJzkXPoZcG6YUpdRSzAAndkxF2m+11v/b+i75BFAMvA18QmsdPO33lzAnhBBCCCGEEBOPNLMUQgghhBBCiAlIwpwQQgghhBBCTEAS5oQQQgghhBBiApIwJ4QQQgghhBATkIQ5IYQQQgghhJiAJMwJIYQQQgghxAQkYU4IIYQQQgghJiAJc0IIIYQQQggxAf1/+2mnZVVtRuoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = [calculate_accuracy(df['Close'].iloc[-test_size:].values, r) for r in results]\n",
    "\n",
    "plt.figure(figsize = (15, 5))\n",
    "for no, r in enumerate(results):\n",
    "    plt.plot(r, label = 'forecast %d'%(no + 1))\n",
    "plt.plot(df['Close'].iloc[-test_size:].values, label = 'true trend', c = 'black')\n",
    "plt.legend()\n",
    "plt.title('average accuracy: %.4f'%(np.mean(accuracies)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "这个baseline来自：https://github.com/huseinzol05/Stock-Prediction-Models"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
